Adding Date to Dashboard

.gitignore

@@ -1,88 +1,16 @@
-# =========================
-# Python
-# =========================
-__pycache__/
-*.py[cod]
-*$py.class
-.ipynb_checkpoints/
-
-# =========================
-# Virtual environments
-# =========================
-env/
-env*/
-venv/
-.venv/
-enarcelona/
-
-# =========================
-# Secrets
-# =========================
+# 1. Broad Ignores
+/Data/*
+/attach/*
+/results/*
+/enarcelona/*
 .env
-*.env
+__pycache__/
+*.pyc
 
-# =========================
-# Patient data / sensitive data
-# =========================
-Data/
-data/raw/
-data/processed/
-data/ground_truth/
-reference/
+# 2. Ignore virtual environments COMPLETELY
+# This must come BEFORE the unignore rule
+env*/
 
-# =========================
-# Generated results and logs
-# =========================
-results/
-results_edss_benchmark/
-*.log
-
-# =========================
-# Large/generated file types
-# =========================
-*.csv
-*.tsv
-*.json
-*.jsonl
-*.xlsx
-*.xls
-*.png
-*.PNG
-*.jpg
-*.jpeg
-*.svg
-*.pdf
-
-# =========================
-# Temporary / backup files
-# =========================
-*.tmp
-*.bak
-*.orig
-.DS_Store
-
-# =========================
-# Keep important code/config/docs
-# =========================
-!README.md
-!requirements.txt
-!*.py
-!*.md
-!*.yml
-!*.yaml
-!*.toml
-
-# Keep prompt templates / schemas if safe to publish
-!prompts/
-!prompts/**
-!attach/
-!attach/*.gbnf
-!attach/just_edss_text.txt
-!attach/Komplett.txt
-
-# Keep example/synthetic data only
-!data/
-!data/example/
-!data/example/**
-!Data/example/
-!Data/example/**
+# 3. The "Unignore" rule (Whitelisting)
+# We only unignore .py files that aren't already blocked by the rules above
+!**/*.py

Data/show_plots.py

@@ -0,0 +1,748 @@
+# %% Scatter
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load your data from TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/join_MS_Briefe_400_with_unique_id_SHA3_explore_cleaned_results+MS_Briefe_400_with_unique_id_SHA3_explore_cleaned.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Replace comma with dot for numeric conversion in GT_EDSS and LLM_Results
+df['GT_EDSS'] = df['GT_EDSS'].astype(str).str.replace(',', '.')
+df['LLM_Results'] = df['LLM_Results'].astype(str).str.replace(',', '.')
+
+# Convert to float (handle invalid entries gracefully)
+df['GT_EDSS'] = pd.to_numeric(df['GT_EDSS'], errors='coerce')
+df['LLM_Results'] = pd.to_numeric(df['LLM_Results'], errors='coerce')
+
+# Drop rows where either column is NaN
+df_clean = df.dropna(subset=['GT_EDSS', 'LLM_Results'])
+
+# Create scatter plot
+plt.figure(figsize=(8, 6))
+plt.scatter(df_clean['GT_EDSS'], df_clean['LLM_Results'], alpha=0.7, color='blue')
+
+# Add labels and title
+plt.xlabel('GT_EDSS')
+plt.ylabel('LLM_Results')
+plt.title('Comparison of GT_EDSS vs LLM_Results')
+
+# Optional: Add a diagonal line for reference (perfect prediction)
+plt.plot([0, max(df_clean['GT_EDSS'])], [0, max(df_clean['GT_EDSS'])], color='red', linestyle='--', label='Perfect Prediction')
+plt.legend()
+
+# Show plot
+plt.grid(True)
+plt.tight_layout()
+plt.show()
+
+##
+
+
+# %% Bland0-altman
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import statsmodels.api as sm
+
+# Load your data from TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/join_MS_Briefe_400_with_unique_id_SHA3_explore_cleaned_results+MS_Briefe_400_with_unique_id_SHA3_explore_cleaned.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Replace comma with dot for numeric conversion in GT_EDSS and LLM_Results
+df['GT_EDSS'] = df['GT_EDSS'].astype(str).str.replace(',', '.')
+df['LLM_Results'] = df['LLM_Results'].astype(str).str.replace(',', '.')
+
+# Convert to float (handle invalid entries gracefully)
+df['GT_EDSS'] = pd.to_numeric(df['GT_EDSS'], errors='coerce')
+df['LLM_Results'] = pd.to_numeric(df['LLM_Results'], errors='coerce')
+
+# Drop rows where either column is NaN
+df_clean = df.dropna(subset=['GT_EDSS', 'LLM_Results'])
+
+# Create Bland-Altman plot
+f, ax = plt.subplots(1, figsize=(8, 5))
+sm.graphics.mean_diff_plot(df_clean['GT_EDSS'], df_clean['LLM_Results'], ax=ax)
+
+# Add labels and title
+ax.set_title('Bland-Altman Plot: GT_EDSS vs LLM_Results')
+ax.set_xlabel('Mean of GT_EDSS and LLM_Results')
+ax.set_ylabel('Difference between GT_EDSS and LLM_Results')
+
+# Display Bland-Altman plot
+plt.tight_layout()
+plt.show()
+
+# Print some statistics
+mean_diff = np.mean(df_clean['GT_EDSS'] - df_clean['LLM_Results'])
+std_diff = np.std(df_clean['GT_EDSS'] - df_clean['LLM_Results'])
+print(f"Mean difference: {mean_diff:.3f}")
+print(f"Standard deviation of differences: {std_diff:.3f}")
+print(f"95% Limits of Agreement: [{mean_diff - 1.96*std_diff:.3f}, {mean_diff + 1.96*std_diff:.3f}]")
+
+##
+
+
+
+# %%  Confusion matrix
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.metrics import confusion_matrix, classification_report
+import seaborn as sns
+
+# Load your data from TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Replace comma with dot for numeric conversion in GT.EDSS and result.EDSS
+df['GT.EDSS'] = df['GT.EDSS'].astype(str).str.replace(',', '.')
+df['result.EDSS'] = df['result.EDSS'].astype(str).str.replace(',', '.')
+
+# Convert to float (handle invalid entries gracefully)
+df['GT.EDSS'] = pd.to_numeric(df['GT.EDSS'], errors='coerce')
+df['result.EDSS'] = pd.to_numeric(df['result.EDSS'], errors='coerce')
+
+# Drop rows where either column is NaN
+df_clean = df.dropna(subset=['GT.EDSS', 'result.EDSS'])
+
+# For confusion matrix, we need to categorize the values
+# Let's create categories up to 10 (0-1, 1-2, 2-3, ..., 9-10)
+def categorize_edss(value):
+    if pd.isna(value):
+        return np.nan
+    elif value <= 1.0:
+        return '0-1'
+    elif value <= 2.0:
+        return '1-2'
+    elif value <= 3.0:
+        return '2-3'
+    elif value <= 4.0:
+        return '3-4'
+    elif value <= 5.0:
+        return '4-5'
+    elif value <= 6.0:
+        return '5-6'
+    elif value <= 7.0:
+        return '6-7'
+    elif value <= 8.0:
+        return '7-8'
+    elif value <= 9.0:
+        return '8-9'
+    elif value <= 10.0:
+        return '9-10'
+    else:
+        return '10+'
+
+# Create categorical versions
+df_clean['GT.EDSS_cat'] = df_clean['GT.EDSS'].apply(categorize_edss)
+df_clean['result.EDSS_cat'] = df_clean['result.EDSS'].apply(categorize_edss)
+
+# Remove any NaN categories
+df_clean = df_clean.dropna(subset=['GT.EDSS_cat', 'result.EDSS_cat'])
+
+# Create confusion matrix
+cm = confusion_matrix(df_clean['GT.EDSS_cat'], df_clean['result.EDSS_cat'],
+                     labels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'])
+
+# Plot confusion matrix
+plt.figure(figsize=(10, 8))
+sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
+            xticklabels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'],
+            yticklabels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'])
+plt.title('Confusion Matrix: Ground truth EDSS vs interferred EDSS (Categorized 0-10)')
+plt.xlabel('LLM Generated EDSS')
+plt.ylabel('Ground Truth EDSS')
+plt.tight_layout()
+plt.show()
+
+# Print classification report
+print("Classification Report:")
+print(classification_report(df_clean['GT.EDSS_cat'], df_clean['result.EDSS_cat']))
+
+# Print raw counts
+print("\nConfusion Matrix (Raw Counts):")
+print(cm)
+
+##
+
+
+
+# %% Classification 
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import confusion_matrix
+import numpy as np
+
+# Load your data from TSV file
+file_path ='/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
+
+df = pd.read_csv(file_path, sep='\t')
+
+# Check data structure
+print("Data shape:", df.shape)
+print("First few rows:")
+print(df.head())
+print("\nColumn names:")
+for col in df.columns:
+    print(f"  {col}")
+
+# Function to safely convert to boolean
+def safe_bool_convert(series):
+    '''Safely convert series to boolean, handling various input formats'''
+    # Convert to string first, then to boolean
+    series_str = series.astype(str).str.strip().str.lower()
+
+    # Handle different true/false representations
+    bool_map = {
+        'true': True, '1': True, 'yes': True, 'y': True,
+        'false': False, '0': False, 'no': False, 'n': False
+    }
+
+    converted = series_str.map(bool_map)
+
+    # Handle remaining NaN values
+    converted = converted.fillna(False)  # or True, depending on your preference
+
+    return converted
+
+# Convert columns safely
+if 'result.klassifizierbar' in df.columns:
+    print("\nresult.klassifizierbar column info:")
+    print(df['result.klassifizierbar'].head(10))
+    print("Unique values:", df['result.klassifizierbar'].unique())
+
+    df['result.klassifizierbar'] = safe_bool_convert(df['result.klassifizierbar'])
+    print("After conversion:")
+    print(df['result.klassifizierbar'].value_counts())
+
+if 'GT.klassifizierbar' in df.columns:
+    print("\nGT.klassifizierbar column info:")
+    print(df['GT.klassifizierbar'].head(10))
+    print("Unique values:", df['GT.klassifizierbar'].unique())
+
+    df['GT.klassifizierbar'] = safe_bool_convert(df['GT.klassifizierbar'])
+    print("After conversion:")
+    print(df['GT.klassifizierbar'].value_counts())
+
+# Create bar chart showing only True values for klassifizierbar
+if 'result.klassifizierbar' in df.columns and 'GT.klassifizierbar' in df.columns:
+    # Get counts for True values only
+    llm_true_count = df['result.klassifizierbar'].sum()
+    gt_true_count = df['GT.klassifizierbar'].sum()
+
+    # Plot using matplotlib directly
+    fig, ax = plt.subplots(figsize=(8, 6))
+
+    x = np.arange(2)
+    width = 0.35
+
+    bars1 = ax.bar(x[0] - width/2, llm_true_count, width, label='LLM', color='skyblue', alpha=0.8)
+    bars2 = ax.bar(x[1] + width/2, gt_true_count, width, label='GT', color='lightcoral', alpha=0.8)
+
+    # Add value labels on bars
+    ax.annotate(f'{llm_true_count}',
+                xy=(x[0], llm_true_count),
+                xytext=(0, 3),
+                textcoords="offset points",
+                ha='center', va='bottom')
+
+    ax.annotate(f'{gt_true_count}',
+                xy=(x[1], gt_true_count),
+                xytext=(0, 3),
+                textcoords="offset points",
+                ha='center', va='bottom')
+
+    ax.set_xlabel('Classification Status (klassifizierbar)')
+    ax.set_ylabel('Count')
+    ax.set_title('True Values Comparison: LLM vs GT for "klassifizierbar"')
+    ax.set_xticks(x)
+    ax.set_xticklabels(['LLM', 'GT'])
+    ax.legend()
+
+    plt.tight_layout()
+    plt.show()
+
+# Create confusion matrix if both columns exist
+if 'result.klassifizierbar' in df.columns and 'GT.klassifizierbar' in df.columns:
+    try:
+        # Ensure both columns are boolean
+        llm_bool = df['result.klassifizierbar'].fillna(False).astype(bool)
+        gt_bool = df['GT.klassifizierbar'].fillna(False).astype(bool)
+
+        cm = confusion_matrix(gt_bool, llm_bool)
+
+        # Plot confusion matrix
+        fig, ax = plt.subplots(figsize=(8, 6))
+        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
+                   xticklabels=['False ', 'True '],
+                   yticklabels=['False', 'True '],
+                   ax=ax)
+        ax.set_xlabel('LLM Predictions ')
+        ax.set_ylabel('GT Labels ')
+        ax.set_title('Confusion Matrix: LLM vs GT for "klassifizierbar"')
+
+        plt.tight_layout()
+        plt.show()
+
+        print("Confusion Matrix:")
+        print(cm)
+
+    except Exception as e:
+        print(f"Error creating confusion matrix: {e}")
+
+# Show final data info
+print("\nFinal DataFrame info:")
+print(df[['result.klassifizierbar', 'GT.klassifizierbar']].info())
+
+##
+
+
+
+
+# %% Boxplot
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+# Load your data from TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/join_results_unique.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Replace comma with dot for numeric conversion in GT.EDSS and result.EDSS
+df['GT.EDSS'] = df['GT.EDSS'].astype(str).str.replace(',', '.')
+df['result.EDSS'] = df['result.EDSS'].astype(str).str.replace(',', '.')
+
+# Convert to float (handle invalid entries gracefully)
+df['GT.EDSS'] = pd.to_numeric(df['GT.EDSS'], errors='coerce')
+df['result.EDSS'] = pd.to_numeric(df['result.EDSS'], errors='coerce')
+
+# Drop rows where either column is NaN
+df_clean = df.dropna(subset=['GT.EDSS', 'result.EDSS'])
+
+# 1. DEFINE CATEGORY ORDER
+# This ensures the X-axis is numerically logical (0-1 comes before 1-2)
+category_order = ['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10', '10+']
+
+# Convert the column to a Categorical type with the specific order
+df_clean['GT.EDSS_cat'] = pd.Categorical(df_clean['GT.EDSS'].apply(categorize_edss), 
+                                         categories=category_order, 
+                                         ordered=True)
+
+plt.figure(figsize=(14, 8))
+
+# 2. ADD HUE FOR LEGEND
+# Assigning x to 'hue' allows Seaborn to generate a legend automatically
+box_plot = sns.boxplot(
+    data=df_clean, 
+    x='GT.EDSS_cat', 
+    y='result.EDSS',
+    hue='GT.EDSS_cat',  # Added hue
+    palette='viridis', 
+    linewidth=1.5,
+    legend=True         # Ensure legend is enabled
+)
+
+# 3. CUSTOMIZE PLOT
+plt.title('Distribution of result.EDSS by GT.EDSS Category', fontsize=18, pad=20)
+plt.xlabel('Ground Truth EDSS Category', fontsize=14)
+plt.ylabel('LLM Predicted EDSS', fontsize=14)
+
+# Move legend to the side or top
+plt.legend(title="EDSS Categories", bbox_to_anchor=(1.05, 1), loc='upper left')
+
+plt.xticks(rotation=45, ha='right', fontsize=10)
+plt.grid(True, axis='y', alpha=0.3)
+plt.tight_layout()
+
+plt.show()
+##
+
+
+# %% Postproccessing Column names
+
+import pandas as pd
+
+# Read the TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Create a mapping dictionary for German to English column names
+column_mapping = {
+    'EDSS':'GT.EDSS',
+    'klassifizierbar': 'GT.klassifizierbar',
+    'Sehvermögen': 'GT.VISUAL_OPTIC_FUNCTIONS',
+    'Cerebellum': 'GT.CEREBELLAR_FUNCTIONS',
+    'Hirnstamm': 'GT.BRAINSTEM_FUNCTIONS',
+    'Sensibiliät': 'GT.SENSORY_FUNCTIONS',
+    'Pyramidalmotorik': 'GT.PYRAMIDAL_FUNCTIONS',
+    'Ambulation': 'GT.AMBULATION',
+    'Cerebrale_Funktion': 'GT.CEREBRAL_FUNCTIONS',
+    'Blasen-_und_Mastdarmfunktion': 'GT.BOWEL_AND_BLADDER_FUNCTIONS'
+}
+
+# Rename columns
+df = df.rename(columns=column_mapping)
+
+# Save the modified dataframe back to TSV file
+df.to_csv(file_path, sep='\t', index=False)
+
+print("Columns have been successfully renamed!")
+print("Renamed columns:")
+for old_name, new_name in column_mapping.items():
+    if old_name in df.columns:
+        print(f"  {old_name} -> {new_name}")
+
+
+##
+
+
+
+
+# %% Styled table
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import dataframe_image as dfi
+# Load data
+df = pd.read_csv("/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv", sep='\t')
+
+# 1. Identify all GT and result columns
+gt_columns = [col for col in df.columns if col.startswith('GT.')]
+result_columns = [col for col in df.columns if col.startswith('result.')]
+
+print("GT Columns found:", gt_columns)
+print("Result Columns found:", result_columns)
+
+# 2. Create proper mapping between GT and result columns
+# Handle various naming conventions (spaces, underscores, etc.)
+column_mapping = {}
+
+for gt_col in gt_columns:
+    base_name = gt_col.replace('GT.', '')
+
+    # Clean the base name for matching - remove spaces, underscores, etc.
+    # Try different matching approaches
+    candidates = [
+        f'result.{base_name}',  # Exact match
+        f'result.{base_name.replace(" ", "_")}',  # With underscores
+        f'result.{base_name.replace("_", " ")}',  # With spaces
+        f'result.{base_name.replace(" ", "")}',   # No spaces
+        f'result.{base_name.replace("_", "")}'    # No underscores
+    ]
+
+    # Also try case-insensitive matching
+    candidates.append(f'result.{base_name.lower()}')
+    candidates.append(f'result.{base_name.upper()}')
+
+    # Try to find matching result column
+    matched = False
+    for candidate in candidates:
+        if candidate in result_columns:
+            column_mapping[gt_col] = candidate
+            matched = True
+            break
+
+    # If no exact match found, try partial matching
+    if not matched:
+        # Try to match by removing special characters and comparing
+        base_clean = ''.join(e for e in base_name if e.isalnum() or e in ['_', ' '])
+        for result_col in result_columns:
+            result_base = result_col.replace('result.', '')
+            result_clean = ''.join(e for e in result_base if e.isalnum() or e in ['_', ' '])
+            if base_clean.lower() == result_clean.lower():
+                column_mapping[gt_col] = result_col
+                matched = True
+                break
+
+print("Column mapping:", column_mapping)
+
+# 3. Faster, vectorized computation using the corrected mapping
+data_list = []
+
+for gt_col, result_col in column_mapping.items():
+    print(f"Processing {gt_col} vs {result_col}")
+
+    # Convert to numeric, forcing errors to NaN
+    s1 = pd.to_numeric(df[gt_col], errors='coerce').astype(float)
+    s2 = pd.to_numeric(df[result_col], errors='coerce').astype(float)
+
+    # Calculate matches (abs difference <= 0.5)
+    diff = np.abs(s1 - s2)
+    matches = (diff <= 0.5).sum()
+
+    # Determine the denominator (total valid comparisons)
+    valid_count = diff.notna().sum()
+
+    if valid_count > 0:
+        percentage = (matches / valid_count) * 100
+    else:
+        percentage = 0
+
+    # Extract clean base name for display
+    base_name = gt_col.replace('GT.', '')
+
+    data_list.append({
+        'GT': base_name,
+        'Match %': round(percentage, 1)
+    })
+
+
+
+
+# 4. Prepare Data
+match_df = pd.DataFrame(data_list)
+# Clean up labels: Replace underscores with spaces and capitalize
+match_df['GT'] = match_df['GT'].str.replace('_', ' ').str.title()
+match_df = match_df.sort_values('Match %', ascending=False)
+
+# 5. Create a "Beautiful" Table using Seaborn Heatmap
+def create_luxury_table(df, output_file="edss_agreement.png"):
+    # Set the aesthetic style
+    sns.set_theme(style="white", font="sans-serif")
+
+    # Prepare data for heatmap
+    plot_data = df.set_index('GT')[['Match %']]
+
+    # Initialize the figure
+    # Height is dynamic based on number of rows
+    fig, ax = plt.subplots(figsize=(8, len(df) * 0.6))
+
+    # Create a custom diverging color map (Deep Red -> Mustard -> Emerald)
+    # This looks more professional than standard 'RdYlGn'
+    cmap = sns.diverging_palette(15, 135, s=80, l=55, as_cmap=True)
+
+    # Draw the heatmap
+    sns.heatmap(
+        plot_data,
+        annot=True,
+        fmt=".1f",
+        cmap=cmap,
+        center=85,      # Centers the color transition
+        vmin=50, vmax=100, # Range of the gradient
+        linewidths=2,
+        linecolor='white',
+        cbar=False,     # Remove color bar for a "table" look
+        annot_kws={"size": 14, "weight": "bold", "family": "sans-serif"}
+    )
+
+    # Styling the Axes (Turning the heatmap into a table)
+    ax.set_xlabel("")
+    ax.set_ylabel("")
+    ax.xaxis.tick_top() # Move "Match %" label to top
+    ax.set_xticklabels(['Agreement (%)'], fontsize=14, fontweight='bold', color='#2c3e50')
+    ax.tick_params(axis='y', labelsize=12, labelcolor='#2c3e50', length=0)
+
+    # Add a thin border around the plot
+    for _, spine in ax.spines.items():
+        spine.set_visible(True)
+        spine.set_color('#ecf0f1')
+
+    plt.title('EDSS Subcategory Consistency Analysis', fontsize=16, pad=40, fontweight='bold', color='#2c3e50')
+
+    # Add a subtle footer
+    plt.figtext(0.5, 0.0, "Tolerance: ±0.5 points",
+                wrap=True, horizontalalignment='center', fontsize=10, color='gray', style='italic')
+
+    # Save with high resolution
+    plt.tight_layout()
+    plt.savefig(output_file, dpi=300, bbox_inches='tight')
+    print(f"Beautiful table saved as {output_file}")
+
+# Execute
+create_luxury_table(match_df)
+
+
+# Run the function
+save_styled_table(match_df)
+# 6. Save as SVG
+
+plt.savefig("agreement_table.svg", format='svg', dpi=300, bbox_inches='tight')
+print("Successfully saved agreement_table.svg")
+
+# Show plot if running in a GUI environment
+plt.show()
+##
+
+
+
+# %% Time Plot
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+from scipy import stats
+
+# Load the TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Extract the inference_time_sec column
+inference_times = df['inference_time_sec'].dropna()  # Remove NaN values
+
+# Calculate statistics
+mean_time = inference_times.mean()
+std_time = inference_times.std()
+median_time = np.median(inference_times)
+
+# Create the histogram
+fig, ax = plt.subplots(figsize=(10, 6))
+
+# Create histogram with bins of 1 second width
+min_time = int(inference_times.min())
+max_time = int(inference_times.max()) + 1
+bins = np.arange(min_time, max_time + 1, 1)  # Bins of 1 second width
+
+# Create histogram with counts (not probability density)
+n, bins, patches = ax.hist(inference_times, bins=bins, color='lightblue', alpha=0.7, edgecolor='black', linewidth=0.5)
+
+# Generate Gaussian curve for fit
+x = np.linspace(inference_times.min(), inference_times.max(), 100)
+# Scale Gaussian to match histogram counts
+gaussian_counts = stats.norm.pdf(x, mean_time, std_time) * len(inference_times) * (bins[1] - bins[0])
+
+# Plot Gaussian fit
+ax.plot(x, gaussian_counts, color='red', linewidth=2, label=f'Gaussian Fit (μ={mean_time:.1f}s, σ={std_time:.1f}s)')
+
+# Add vertical lines for mean and median
+ax.axvline(mean_time, color='blue', linestyle='--', linewidth=2, label=f'Mean = {mean_time:.1f}s')
+ax.axvline(median_time, color='green', linestyle='--', linewidth=2, label=f'Median = {median_time:.1f}s')
+
+# Add standard deviation as vertical lines
+ax.axvline(mean_time + std_time, color='saddlebrown', linestyle=':', linewidth=1, alpha=0.7, label=f'+1σ = {mean_time + std_time:.1f}s')
+ax.axvline(mean_time - std_time, color='saddlebrown', linestyle=':', linewidth=1, alpha=0.7, label=f'-1σ = {mean_time - std_time:.1f}s')
+
+ax.set_xlabel('Inference Time (seconds)')
+ax.set_ylabel('Frequency')
+ax.set_title('Inference Time Distribution with Gaussian Fit')
+ax.legend()
+ax.grid(True, alpha=0.3)
+
+plt.tight_layout()
+plt.show()
+
+##
+
+
+
+
+
+
+# %% Dashboard 
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from datetime import datetime
+import numpy as np
+
+# Load the data
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Rename columns to remove 'result.' prefix and handle spaces
+column_mapping = {}
+for col in df.columns:
+    if col.startswith('result.'):
+        new_name = col.replace('result.', '')
+        # Handle spaces in column names (replace with underscores if needed)
+        new_name = new_name.replace(' ', '_')
+        column_mapping[col] = new_name
+df = df.rename(columns=column_mapping)
+
+# Convert MedDatum to datetime
+df['MedDatum'] = pd.to_datetime(df['MedDatum'])
+
+# Check what columns actually exist in the dataset
+print("Available columns:")
+print(df.columns.tolist())
+print("\nFirst few rows:")
+print(df.head())
+
+# Hardcode specific patient names
+patient_names = ['bc55b1b2']
+
+# Define the functional systems (columns to plot) - adjust based on actual column names
+functional_systems = ['EDSS', 'Visual', 'Sensory', 'Motor', 'Brainstem', 'Cerebellar', 'Autonomic', 'Bladder', 'Intellectual']
+
+# Create subplots horizontally (2 columns, adjust rows as needed)
+num_plots = len(functional_systems)
+num_cols = 2
+num_rows = (num_plots + num_cols - 1) // num_cols  # Ceiling division
+
+fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 4*num_rows), sharex=False)  # Changed sharex=False
+if num_plots == 1:
+    axes = [axes]
+elif num_rows == 1:
+    axes = axes
+else:
+    axes = axes.flatten()
+
+# Plot for the hardcoded patient
+for i, system in enumerate(functional_systems):
+    # Filter data for this specific patient
+    patient_data = df[df['unique_id'] == patient_names[0]].sort_values('MedDatum')
+
+    # Check if patient data exists
+    if patient_data.empty:
+        print(f"No data found for patient: {patient_names[0]}")
+        continue
+
+    # Check if the system column exists in the data
+    if system in patient_data.columns:
+        # Plot the specific functional system
+        if not patient_data[system].isna().all():
+            axes[i].plot(patient_data['MedDatum'], patient_data[system], marker='o', linewidth=2, label=system)
+            axes[i].set_ylabel('Score')
+            axes[i].set_title(f'Functional System: {system}')
+            axes[i].grid(True, alpha=0.3)
+            axes[i].legend()
+        else:
+            axes[i].set_title(f'Functional System: {system} (No data)')
+            axes[i].set_ylabel('Score')
+            axes[i].grid(True, alpha=0.3)
+    else:
+        # Try to find column with similar name (case insensitive)
+        found_column = None
+        for col in df.columns:
+            if system.lower() in col.lower():
+                found_column = col
+                break
+
+        if found_column:
+            print(f"Found similar column: {found_column}")
+            if not patient_data[found_column].isna().all():
+                axes[i].plot(patient_data['MedDatum'], patient_data[found_column], marker='o', linewidth=2, label=found_column)
+                axes[i].set_ylabel('Score')
+                axes[i].set_title(f'Functional System: {system} (found as: {found_column})')
+                axes[i].grid(True, alpha=0.3)
+                axes[i].legend()
+        else:
+            axes[i].set_title(f'Functional System: {system} (Column not found)')
+            axes[i].set_ylabel('Score')
+            axes[i].grid(True, alpha=0.3)
+
+# Hide empty subplots
+for i in range(len(functional_systems), len(axes)):
+    axes[i].set_visible(False)
+
+# Set x-axis label for the last row only
+for i in range(len(functional_systems)):
+    if i >= len(axes) - num_cols:  # Last row
+        axes[i].set_xlabel('Date')
+
+# Force date formatting on all axes
+for ax in axes:
+    ax.tick_params(axis='x', rotation=45)
+    ax.xaxis.set_major_formatter(plt.matplotlib.dates.DateFormatter('%Y-%m-%d'))
+    ax.xaxis.set_major_locator(plt.matplotlib.dates.MonthLocator())
+
+# Automatically format x-axis dates
+plt.gcf().autofmt_xdate()
+
+plt.tight_layout()
+plt.show()
+
+##

Data/style2.py

@@ -0,0 +1,135 @@
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import dataframe_image as dfi
+# Load data
+df = pd.read_csv("/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv", sep='\t')
+
+# 1. Identify all GT and result columns
+gt_columns = [col for col in df.columns if col.startswith('GT.')]
+result_columns = [col for col in df.columns if col.startswith('result.')]
+
+print("GT Columns found:", gt_columns)
+print("Result Columns found:", result_columns)
+
+# 2. Create proper mapping between GT and result columns
+# Handle various naming conventions (spaces, underscores, etc.)
+column_mapping = {}
+
+for gt_col in gt_columns:
+    base_name = gt_col.replace('GT.', '')
+
+    # Clean the base name for matching - remove spaces, underscores, etc.
+    # Try different matching approaches
+    candidates = [
+        f'result.{base_name}',  # Exact match
+        f'result.{base_name.replace(" ", "_")}',  # With underscores
+        f'result.{base_name.replace("_", " ")}',  # With spaces
+        f'result.{base_name.replace(" ", "")}',   # No spaces
+        f'result.{base_name.replace("_", "")}'    # No underscores
+    ]
+
+    # Also try case-insensitive matching
+    candidates.append(f'result.{base_name.lower()}')
+    candidates.append(f'result.{base_name.upper()}')
+
+    # Try to find matching result column
+    matched = False
+    for candidate in candidates:
+        if candidate in result_columns:
+            column_mapping[gt_col] = candidate
+            matched = True
+            break
+
+    # If no exact match found, try partial matching
+    if not matched:
+        # Try to match by removing special characters and comparing
+        base_clean = ''.join(e for e in base_name if e.isalnum() or e in ['_', ' '])
+        for result_col in result_columns:
+            result_base = result_col.replace('result.', '')
+            result_clean = ''.join(e for e in result_base if e.isalnum() or e in ['_', ' '])
+            if base_clean.lower() == result_clean.lower():
+                column_mapping[gt_col] = result_col
+                matched = True
+                break
+
+print("Column mapping:", column_mapping)
+
+# 3. Faster, vectorized computation using the corrected mapping
+data_list = []
+
+for gt_col, result_col in column_mapping.items():
+    print(f"Processing {gt_col} vs {result_col}")
+
+    # Convert to numeric, forcing errors to NaN
+    s1 = pd.to_numeric(df[gt_col], errors='coerce').astype(float)
+    s2 = pd.to_numeric(df[result_col], errors='coerce').astype(float)
+
+    # Calculate matches (abs difference <= 0.5)
+    diff = np.abs(s1 - s2)
+    matches = (diff <= 0.5).sum()
+
+    # Determine the denominator (total valid comparisons)
+    valid_count = diff.notna().sum()
+
+    if valid_count > 0:
+        percentage = (matches / valid_count) * 100
+    else:
+        percentage = 0
+
+    # Extract clean base name for display
+    base_name = gt_col.replace('GT.', '')
+
+    data_list.append({
+        'GT': base_name,
+        'Match %': round(percentage, 1)
+    })
+
+
+
+# 4. Prepare Data for Plotting
+match_df = pd.DataFrame(data_list)
+match_df = match_df.sort_values('Match %', ascending=False) # Sort for better visual flow
+
+# 5. Create the Styled Gradient Table
+def style_agreement_table(df):
+    return (df.style
+        .format({'Match %': '{:.1f}%'}) # Add % sign
+        .background_gradient(cmap='RdYlGn', subset=['Match %'], vmin=50, vmax=100) # Red to Green gradient
+        .set_properties(**{
+            'text-align': 'center',
+            'font-size': '12pt',
+            'border-collapse': 'collapse',
+            'border': '1px solid #D3D3D3'
+        })
+        .set_table_styles([
+            # Style the header
+            {'selector': 'th', 'props': [
+                ('background-color', '#404040'), 
+                ('color', 'white'),
+                ('font-weight', 'bold'),
+                ('text-transform', 'uppercase'),
+                ('padding', '10px')
+            ]},
+            # Add hover effect
+            {'selector': 'tr:hover', 'props': [('background-color', '#f5f5f5')]}
+        ])
+        .set_caption("EDSS Agreement Analysis: Ground Truth vs. Results (Tolerance ±0.5)")
+    )
+
+# To display in a Jupyter Notebook:
+styled_table = style_agreement_table(match_df)
+styled_table
+
+dfi.export(styled_table, "styled_table.png")
+#styled_table.to_html("agreement_report.html")
+# 6. Save as SVG
+
+#plt.savefig("agreement_table.svg", format='svg', dpi=300, bbox_inches='tight')
+#print("Successfully saved agreement_table.svg")
+
+# Show plot if running in a GUI environment
+plt.show()
+
+

Data/styled_tables.py

@@ -0,0 +1,74 @@
+import pandas as pd
+import numpy as np
+import seaborn as sns
+
+# Sample data (replace with your actual df)
+df = pd.read_csv("/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv", sep='\t')
+
+# Identify GT and Result columns
+gt_columns = [col for col in df.columns if col.startswith('GT.')]
+result_columns = [col for col in df.columns if col.startswith('result.')]
+
+# Create mapping
+column_mapping = {}
+for gt_col in gt_columns:
+    base_name = gt_col.replace('GT.', '')
+    result_col = f'result.{base_name}'
+    if result_col in result_columns:
+        column_mapping[gt_col] = result_col
+
+# Function to compute match percentage for each GT-Result pair
+def compute_match_percentages(df, column_mapping):
+    percentages = []
+    for gt_col, result_col in column_mapping.items():
+        count = 0
+        total = len(df)
+
+        for _, row in df.iterrows():
+            gt_val = row[gt_col]
+            result_val = row[result_col]
+
+            # Handle NaN values
+            if pd.isna(gt_val) or pd.isna(result_val):
+                continue
+
+            # Handle non-numeric values
+            try:
+                gt_float = float(gt_val)
+                result_float = float(result_val)
+            except (ValueError, TypeError):
+                # Skip rows with non-numeric values
+                continue
+
+            # Check if values are within 0.5 tolerance
+            if abs(gt_float - result_float) <= 0.5:
+                count += 1
+
+        percentage = (count / total) * 100
+        percentages.append({
+            'GT_Column': gt_col,
+            'Result_Column': result_col,
+            'Match_Percentage': round(percentage, 1)
+        })
+
+    return pd.DataFrame(percentages)
+
+# Compute match percentages
+match_df = compute_match_percentages(df, column_mapping)
+
+# Create a pivot table for gradient display (optional but helpful)
+pivot_table = match_df.set_index(['GT_Column', 'Result_Column'])['Match_Percentage'].unstack(fill_value=0)
+
+# Apply gradient background
+cm = sns.light_palette("green", as_cmap=True)
+styled_table = pivot_table.style.background_gradient(cmap=cm, axis=None)
+
+# Display result
+print("Agreement Percentage Table (with gradient):")
+styled_table
+
+
+
+# Save the styled table to a file
+styled_table.to_html("agreement_report.html")
+print("Report saved to agreement_report.html")

PROJECT_STRUCTURE.md

@@ -1,31 +0,0 @@
-# Project Structure
-
-This project was reorganized into:
-
-- `data/`
-  - `raw/`: original raw data, if retained locally
-  - `processed/`: cleaned or derived input data
-  - `ground_truth/`: manually annotated reference data
-  - `external/`: externally provided data
-
-- `prompts/`
-  - EDSS instructions and prompt/schema assets
-
-- `scripts/`
-  - runnable analysis and plotting scripts
-
-- `results/`
-  - `benchmark_runs/`: full model benchmark runs
-  - `final_results/`: final selected model outputs
-  - `figures/`: generated figures
-  - `tables/`: generated tables
-  - `logs/`: terminal logs
-
-- `manuscript/`
-  - final figures and tables for paper/thesis writing
-
-- `archive/`
-  - old scripts, old results, temporary files, and unclear legacy files
-
-Important:
-The reorganization was performed after creating a full timestamped backup.

app.py

@@ -216,3 +216,6 @@ if __name__ == "__main__":
 
 
 
+# %% name
+eXXXXXXXX
+##

figure1.py

@@ -263,120 +263,3 @@ plt.legend(frameon=False, loc='upper center', bbox_to_anchor=(0.5, -0.05))
 plt.tight_layout()
 plt.show()
 ##
-
-
-
-
-# %% name
-import matplotlib.pyplot as plt
-
-# Data
-data = {
-    'Visit': [9, 8, 7, 6, 5, 4, 3, 2, 1],
-    'patient_count': [2, 3, 3, 6, 13, 17, 28, 24, 32]
-}
-
-# Create figure and axis
-fig, ax = plt.subplots(figsize=(10, 6))
-
-# Plot the bar chart
-bars = ax.bar(data['Visit'], data['patient_count'], color='darkblue', label='Patients by Visit Count')
-
-# Add labels and title
-ax.set_xlabel('Visit Number (from last to first)', fontsize=12)
-ax.set_ylabel('Number of Patients', fontsize=12)
-ax.set_title('Patient Visits by Visit Number', fontsize=14)
-
-# Invert x-axis to show Visit 9 on the left (descending order) if desired, but keep natural order (1–9 left to right)
-# For descending order (9→1 from left to right), we'd need to reverse:
-# Visit = data['Visit'][::-1], patient_count = data['patient_count'][::-1]
-# But standard practice is ascending (1 to 9), so we'll sort accordingly:
-# Let's sort by Visit to ensure left-to-right: 1,2,...,9
-
-# Actually, your current Visit list is [9,8,...,1], which is descending.
-# Let's sort by Visit for intuitive left-to-right increasing order:
-sorted_indices = sorted(range(len(data['Visit'])), key=lambda i: data['Visit'][i])
-visit_sorted = [data['Visit'][i] for i in sorted_indices]
-count_sorted = [data['patient_count'][i] for i in sorted_indices]
-
-# Re-plot with sorted x-axis:
-ax.clear()
-bars = ax.bar(visit_sorted, count_sorted, color='darkblue', label='Patients by Visit Count')
-
-# Re-apply labels, etc.
-ax.set_xlabel('Number of Visits', fontsize=12)
-ax.set_ylabel('Number of Unique Patients', fontsize=12)
-#ax.set_title('Number of Patients by Visit Number', fontsize=14)
-
-# Add legend
-ax.legend()
-
-# Improve layout and grid
-ax.grid(axis='y', linestyle='--', alpha=0.7)
-plt.xticks(visit_sorted)  # Ensure all integer visit numbers are shown
-
-# Show the plot
-plt.tight_layout()
-plt.show()
-
-##
-
-# %% Patientjourney Bubble chart
-import matplotlib.pyplot as plt
-import numpy as np
-
-import matplotlib as mpl
-
-mpl.rcParams["font.family"] = "DejaVu Sans"   # or "Arial", "Calibri", "Times New Roman", ...
-mpl.rcParams["font.size"] = 12                # default size for text
-mpl.rcParams["axes.titlesize"] = 14
-mpl.rcParams["axes.titleweight"] = "bold"
-
-
-# Data (your counts)
-visits = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
-patient_count = np.array([32, 24, 28, 17, 13, 6, 3, 3, 2])
-
-# "Remaining" = patients with >= that many visits (cumulative from the right)
-remaining = np.array([patient_count[i:].sum() for i in range(len(patient_count))])
-
-# --- Plot ---
-fig, ax = plt.subplots(figsize=(12, 3))
-
-y = 0.0  # all bubbles on one horizontal line
-
-# Horizontal line
-ax.hlines(y, visits.min() - 0.4, visits.max() + 0.4, color="#1f77b4", linewidth=3)
-
-# Bubble sizes (scale as needed)
-# (Matplotlib scatter uses area in points^2)
-sizes = patient_count * 35  # tweak this multiplier if you want bigger/smaller bubbles
-
-ax.scatter(visits, np.full_like(visits, y), s=sizes, color="#1f77b4", zorder=3)
-
-# Title
-#ax.set_title("Patient Journey by Visit Count", fontsize=14, pad=18)
-
-# Top labels: "1 visits", "2 visits", ...
-for x in visits:
-    label = f"{x} visit" if x == 1 else f"{x} visits"
-    ax.text(x, y + 0.18, label, ha="center", va="bottom", fontsize=10)
-
-# Bottom labels: "X patients" and "Y remaining"
-for x, pc, rem in zip(visits, patient_count, remaining):
-    ax.text(x, y - 0.20, f"{pc} patients", ha="center", va="top", fontsize=9)
-    ax.text(x, y - 0.32, f"{rem} remaining", ha="center", va="top", fontsize=9)
-
-# Cosmetics: remove axes, keep spacing nice
-ax.set_xlim(visits.min() - 0.6, visits.max() + 0.6)
-ax.set_ylim(-0.5, 0.35)
-ax.set_xticks([])
-ax.set_yticks([])
-for spine in ax.spines.values():
-    spine.set_visible(False)
-
-plt.tight_layout()
-plt.show()
-plt.savefig("patient_journey.svg", format="svg", bbox_inches="tight")
-##
-

prompts/Komplett.txt

@@ -1,481 +0,0 @@
-1 VISUAL OPTIC FUNCTIONS
-
-VISUAL ACUITY
-The visual acuity score is based on the line in the Snellen chart at 20 feet 5 meters
-for which the patient makes no more than one error using best available correction
-Alternatively best corrected near vision can be assessed but this should be noted and
-consistently performed during follow up examinations Switching from near to distance
-visual acuity measurements should be avoided in follow up examinations
-
-VISUAL FIELDS
-0 normal
-1 signs only deficits present only on formal confrontational testing
-2 moderate patient aware of deficit but incomplete hemianopsia on examination
-3 marked complete homonymous hemianopsia or equivalent
-
-SCOTOMA
-0 none
-1 small detectable only on formal confrontational testing
-2 large spontaneously reported by patient
-
-* DISC PALLOR
-0 not present
-1 present
-
-NOTE
-When determining the EDSS step the Visual FS score must be converted to a lower
-score as follows
-Visual FS Score 6 5 4 3 2 1
-Converted Visual FS Score 4 3 3 2 2 1
-
-FUNCTIONAL SYSTEM SCORE
-0 normal
-1 disc pallor and or small scotoma and or visual acuity corrected of worse eye less than 20 20 1.0 but better than 20 30 0.67
-2 worse eye with maximal visual acuity corrected of 20 30 to 20 59 0.67 – 0.34
-3 worse eye with large scotoma and or moderate decrease in fields and or maximal visual acuity corrected of 20 60 to 20 99 0.33 – 0.21
-4 worse eye with marked decrease of fields and or maximal visual acuity corrected of 20 100 to 20 200 0.2 – 0.1 grade 3 plus maximal acuity of better eye of 20 60 0.33 or less
-5 worse eye with maximal visual acuity corrected less than 20 200 0.1 grade 4 plus maximal acuity of better eye of 20 60 0.33 or less
-6 grade 5 plus maximal acuity of better eye of 20 60 0.33 or less *  =  optional part of the examination
-
-
-### BRAINSTEM FUNCTIONS
-
-**DYSARTHRIA**
-- **0**: None
-- **1**: Signs only
-- **2**: Mild: Clinically detectable, patient is aware
-- **3**: Moderate: Obvious during conversation, impairs comprehension
-- **4**: Marked: Incomprehensible speech
-- **5**: Inability to speak
-
-**DYSPHAGIA**
-- **0**: None
-- **1**: Signs only
-- **2**: Mild: Difficulty with thin liquids
-- **3**: Moderate: Difficulty with liquids and solid food
-- **4**: Marked: Sustained difficulty, requires pureed diet
-- **5**: Inability to swallow
-
-**OTHER CRANIAL NERVE FUNCTIONS**
-- **0**: Normal
-- **1**: Signs only
-- **2**: Mild disability: Clinically detectable deficit, patient is usually aware
-- **3**: Moderate disability
-- **4**: Marked disability
-
-**EXTRAOCULAR MOVEMENTS (EOM) IMPAIRMENT**
-- **0**: None
-- **1**: Signs only: Subtle EOM weakness, no complaints of vision issues
-- **2**: Mild: Subtle EOM weakness or obvious incomplete paralysis not noticed by patient
-- **3**: Moderate: Obvious incomplete paralysis noticed by patient or complete loss in one direction
-- **4**: Marked: Complete loss in more than one direction
-
-**NYSTAGMUS**
-- **0**: None
-- **1**: Signs only or mild: Gaze-evoked nystagmus below moderate limits (equivalent to Brainstem FS score of 1)
-- **2**: Moderate: Sustained nystagmus on horizontal/vertical gaze at 30 degrees, patient may not notice
-- **3**: Severe: Nystagmus in primary position or coarse persistent nystagmus interfering with vision; complete internuclear ophthalmoplegia; oscillopsia
-
-**TRIGEMINAL DAMAGE**
-- **0**: None
-- **1**: Signs only
-- **2**: Mild: Clinically detectable numbness, patient is aware
-- **3**: Moderate: Impaired sharp/dull discrimination in one to three branches or trigeminal neuralgia (at least one recent attack)
-- **4**: Marked: Unable to discriminate between sharp/dull or complete loss of sensation in one or both nerves
-
-**FACIAL WEAKNESS**
-- **0**: None
-- **1**: Signs only
-- **2**: Mild: Clinically detectable weakness, patient is aware
-- **3**: Moderate: Incomplete facial palsy (e.g., eye closure requires patching, drooling)
-- **4**: Marked: Complete unilateral or bilateral facial palsy with lagophthalmus or difficulty with liquids
-
-**HEARING LOSS**
-- **0**: None
-- **1**: Signs only: Hears finger rub less on one/both sides, lateralized Weber test but no complaints
-- **2**: Mild: As in 1, aware of hearing problem
-- **3**: Moderate: Does not hear finger rub on one/both sides, misses several whispered numbers
-- **4**: Marked: Misses all or nearly all whispered numbers
-
-**FUNCTIONAL SYSTEM SCORE**
-- **0**: Normal
-- **1**: Signs only
-- **2**: Moderate nystagmus/EOM impairment/other mild disability
-- **3**: Severe nystagmus/marked EOM impairment/moderate other cranial nerve disability
-- **4**: Marked dysarthria/other marked disability
-- **5**: Inability to swallow or speak
-
-### PYRAMIDAL FUNCTIONS
-
-#### REFLEXES
-- **0**: Absent
-- **1**: Diminished
-- **2**: Normal
-- **3**: Exaggerated
-- **4**: Nonsustained clonus (a few beats of clonus)
-- **5**: Sustained clonus
-
-##### Cutaneous Reflexes
-- **0**: Normal
-- **1**: Weak
-- **2**: Absent
-
-###### Palmomental Reflex
-- **0**: Absent
-- **1**: Present
-
-###### Plantar Response
-- **0**: Flexor
-- **1**: Neutral or equivocal
-- **2**: Extensor
-
-#### LIMB STRENGTH
-The weakest muscle in each group defines the score for that muscle group. Optional functional tests (hopping on one foot and walking on heels/toes) are recommended for BMRC grades 3–5.
-
-##### BMRC Rating Scale
-- **0**: No muscle contraction detected
-- **1**: Visible contraction without visible joint movement
-- **2**: Visible movement only on the plane of gravity
-- **3**: Active movement against gravity, but not against resistance
-- **4**: Active movement against resistance, but not full strength
-- **5**: Normal strength
-
-#### FUNCTIONAL TESTS
-##### Pronator Drift (Upper Extremities)
-Pronation and downward drift:
-- **0**: None
-- **1**: Mild
-- **2**: Evident
-
-##### Position Test (Lower Extremities)
-Ask patient to lift both legs together, with legs fully extended at the knee. Sinking:
-- **0**: None
-- **1**: Mild
-- **2**: Evident
-- **3**: Able to lift only one leg at a time (grade from the horizontal position at the hip joints in degrees)
-- **4**: Unable to lift one leg at a time
-
-##### Walking on Heels/Toes
-- **0**: Normal
-- **1**: Impaired
-- **2**: Not possible
-
-##### Hopping on One Foot
-- **0**: Normal
-- **1**: 6–10 times
-- **2**: 1–5 times
-- **3**: Not possible
-
-#### LIMB SPASTICITY (AFTER RAPID FLEXION OF THE EXTREMITY)
-- **0**: None
-- **1**: Mild: barely increased muscle tone
-- **2**: Moderate: moderately increased muscle tone that can be overcome; full range of motion is possible
-- **3**: Severe: severely increased muscle tone that is extremely difficult to overcome; full range of motion is not possible
-- **4**: Contracted
-
-#### GAIT SPASTICITY
-- **0**: None
-- **1**: Barely perceptible
-- **2**: Evident: minor interference with function
-- **3**: Permanent shuffling: major interference with function
-
-#### OVERALL MOTOR PERFORMANCE
-- **0**: Normal
-- **1**: Abnormal weakness (as compared to peers) in performing more demanding tasks, e.g., walking longer distances; no reduction in limb strength on formal testing
-- **2**: Reduction in strength of individual muscle groups at confrontational testing
-
-#### FUNCTIONAL SYSTEM SCORE
-- **0**: Normal
-- **1**: Abnormal signs without disability
-- **2**: Minimal disability: patient complains of motor-fatigability or reduced performance in strenuous motor tasks (motor performance grade 1) and/or BMRC grade 4 in one or two muscle groups
-- **3**: Mild to moderate paraparesis or hemiparesis: usually BMRC grade 4 in more than two muscle groups; and/or BMRC grade 3 in one or two muscle groups (movements against gravity
-are possible); and/or severe monoparesis: BMRC grade 2 or less in one muscle group
-- **4**: Marked paraparesis or hemiparesis: usually BMRC grade 2 in two limbs or monoplegia with BMRC grade 0 or 1 in one limb; and/or moderate tetraparesis: BMRC grade 3 in three or more limbs
-- **5**: Paraplegia: BMRC grade 0 or 1 in all muscle groups of the lower limbs; and/or marked tetraparesis: BMRC grade 2 or less in three or more limbs; and/or hemiplegia
-- **6**: Tetraplegia: BMRC grade 0 or 1 in all muscle groups of the upper and lower limbs
-
-### CEREBELLAR FUNCTIONS
-
-#### HEAD TREMOR
-- **0**: none
-- **1**: mild
-- **2**: moderate
-- **3**: severe
-
-#### TRUNCAL ATAXIA
-- **0**: none
-- **1**: signs only
-- **2**: mild (swaying with eyes closed)
-- **3**: moderate (swaying with eyes open)
-- **4**: severe (unable to sit without assistance)
-
-#### LIMB ATAXIA (TREMOR / DYSMETRIA AND RAPID ALTERNATING MOVEMENTS)
-- **0**: none
-- **1**: signs only
-- **2**: mild (tremor or clumsy movements easily seen, minor interference with function)
-- **3**: moderate (tremor or clumsy movements interfere with function in all spheres)
-- **4**: severe (most functions are very difficult)
-
-#### TANDEM (STRAIGHT LINE) WALKING
-- **0**: normal
-- **1**: impaired
-- **2**: not possible
-
-#### GAIT ATAXIA
-- **0**: none
-- **1**: signs only
-- **2**: mild (problems with balance realized by patient and/or significant other)
-- **3**: moderate (abnormal balance with ordinary walking)
-- **4**: severe (unable to walk more than a few steps unassisted or requires a walking aid or assistance due to ataxia)
-
-#### ROMBERG TEST
-- **0**: normal
-- **1**: mild (mild instability with eyes closed)
-- **2**: moderate (not stable with eyes closed)
-- **3**: severe (not stable with eyes open)
-
-#### OTHER CEREBELLAR TESTS
-- **0**: normal
-- **1**: mild abnormality
-- **2**: moderate abnormality
-- **3**: severe abnormality
-
-**NOTE:**
-- The presence of severe gait and/or truncal ataxia alone (without severe ataxia in three or four limbs) results in a Cerebellar FS score of 3.
-- If weakness or sensory deficits interfere with the testing of ataxia, score the patient’s actual performance. Indicate the possible role of weakness by marking an "X" after the
-affected subsystems and Cerebellar FS score.
-
-#### FUNCTIONAL SYSTEM SCORE
-- **0**: normal
-- **1**: abnormal signs without disability
-- **2**: mild ataxia and/or moderate station ataxia (Romberg) and/or tandem walking not possible
-- **3**: moderate limb ataxia and/or moderate or severe gait/truncal ataxia
-- **4**: severe gait/truncal ataxia and severe ataxia in three or four limbs
-- **5**: unable to perform coordinated movements due to ataxia
-- **X**: pyramidal weakness (BMRC grade 3 or worse in limb strength) or sensory deficits interfere with cerebellar testing
-
-### SENSORY FUNCTIONS
-
-#### SUPERFICIAL SENSATION (LIGHT TOUCH AND PAIN)
-- **0**: normal
-- **1**: signs only (slightly diminished sensation on formal testing, patient not aware)
-- **2**: mild (patient aware of impaired light touch or pain but can discriminate sharp/dull)
-- **3**: moderate (impaired discrimination of sharp/dull)
-- **4**: marked (unable to discriminate between sharp/dull and/or unable to feel light touch)
-- **5**: complete loss (anesthesia)
-
-#### VIBRATION SENSE (AT THE MOST DISTAL JOINT)
-- **0**: normal
-- **1**: mild (graded tuning fork 5–7 of 8; detects more than 10 seconds but less than examiner)
-- **2**: moderate (graded tuning fork 1–4 of 8; detects between 2 and 10 sec.)
-- **3**: marked (complete loss of vibration sense)
-
-#### POSITION SENSE
-- **0**: normal
-- **1**: mild (1–2 incorrect responses, only distal joints affected)
-- **2**: moderate (misses many movements of fingers or toes; proximal joints affected)
-- **3**: marked (no perception of movement, astasia)
-
-* **LHERMITTE’S SIGN** (does not contribute to the Sensory FS score)
-  - **0**: negative
-  - **1**: positive
-
-* **PARAESTHESIAE (TINGLING)** (does not contribute to the Sensory FS score)
-  - **0**: none
-  - **1**: present
-
-#### FUNCTIONAL SYSTEM SCORE
-- **0**: normal
-- **1**: impaired superficial sensation in one or two limbs
-- **2**: mild impairment in more than two limbs, no major proprioceptive deficits
-- **3**: moderate impairment in more than two limbs with minor proprioceptive deficits
-- **4**: severe impairment in more than two limbs with significant proprioceptive deficits
-- **5**: loss of sensation in one or two limbs, significant proprioceptive deficits in most of the body below the head
-- **6**: essentially no sensation below the head
-
-### BOWEL AND BLADDER FUNCTIONS
-
-#### URINARY HESITANCY AND RETENTION
-- **0**: none
-- **1**: mild (no major impact on lifestyle)
-- **2**: moderate (urinary retention; frequent urinary tract infections)
-- **3**: severe (requires catheterization)
-- **4**: loss of function (overflow incontinence)
-
-#### URINARY URGENCY AND INCONTINENCE
-- **0**: none
-- **1**: mild (no major impact on lifestyle)
-- **2**: moderate (rare incontinence occurring no more than once a week; must wear pads)
-- **3**: severe (frequent incontinence occurring from several times a week to more than once a day; must wear urinal or pads)
-- **4**: loss of function (loss of bladder control)
-
-#### BLADDER CATHETERIZATION
-- **0**: none
-- **1**: intermittent self-catheterization
-- **2**: constant catheterization
-
-#### BOWEL DYSFUNCTION
-- **0**: none
-- **1**: mild (no incontinence, no major impact on lifestyle, mild constipation)
-- **2**: moderate (must wear pads or alter lifestyle to be near lavatory)
-- **3**: severe (in need of enemas or manual measures to evacuate bowels)
-- **4**: complete loss of function
-
-#### SEXUAL DYSFUNCTION
-**Male**
-- **0**: none
-- **1**: mild (difficulty maintaining erection during intercourse, but achieves erection and still has intercourse)
-- **2**: moderate (difficulty achieving erection, decreased libido, still has intercourse and reaches orgasm)
-- **3**: severe (marked decrease in libido, inability to achieve full erection, intercourse with difficulty, hypoorgasmia)
-- **4**: loss of function
-
-**Female**
-- **0**: none
-- **1**: mild (mild lack of lubrication, still sexually active and reaches orgasm)
-- **2**: moderate (dyspareunia, hypoorgasmia, decrease in sexual activity)
-- **3**: severe (marked decrease in sexual activity, anorgasmia)
-- **4**: loss of function
-
-**NOTE**
-When determining the EDSS step, the Bowel and Bladder FS score must be converted to a lower score as follows:
-- Bowel and Bladder FS Score: 6 → Converted Bowel and Bladder FS Score: 5
-- Bowel and Bladder FS Score: 5 → Converted Bowel and Bladder FS Score: 4
-- Bowel and Bladder FS Score: 4 → Converted Bowel and Bladder FS Score: 3
-- Bowel and Bladder FS Score: 3 → Converted Bowel and Bladder FS Score: 3
-- Bowel and Bladder FS Score: 2 → Converted Bowel and Bladder FS Score: 2
-- Bowel and Bladder FS Score: 1 → Converted Bowel and Bladder FS Score: 1
-
-Sexual dysfunction can be documented but generally does not impact the FS score due to assessment difficulties by examining physicians.
-
-### FUNCTIONAL SYSTEM SCORE
-- **0**: normal
-- **1**: mild urinary hesitancy, urgency, and/or constipation
-- **2**: moderate urinary hesitancy/retention and/or moderate urinary urgency/incontinence and/or moderate bowel dysfunction
-- **3**: frequent urinary incontinence or intermittent self-catheterization; needs enemas or manual measures to evacuate bowels
-- **4**: in need of almost constant catheterization
-- **5**: loss of bladder or bowel function (external or indwelling catheter)
-- **6**: loss of bowel and bladder function
-
-### CEREBRAL FUNCTIONS
-
-#### DEPRESSION AND EUPHORIA
-- **0**: none
-- **1**: present (Patient complains of depression or is considered depressed or euphoric by the investigator or significant other.)
-
-**Note**: Depression and Euphoria are documented on the scoring sheet but are not taken into consideration for FS and EDSS calculation.
-
-#### DECREASE IN MENTATION
-- **0**: none
-- **1**: signs only (not apparent to patient and/or significant other)
-- **2**: mild (Patient and/or significant other report mild changes in mentation. Examples include: impaired ability to follow a rapid course of association or survey complex matters;
-impaired judgment in certain demanding situations; capable of handling routine daily activities, but unable to tolerate additional stressors; intermittently symptomatic even with
-normal levels of stress; reduced performance; tendency toward negligence due to obliviousness or fatigue.)
-- **3**: moderate (Definite abnormalities on brief mental status testing, but still oriented to person, place, and time)
-- **4**: marked (Not oriented in one or two spheres (person, place, or time); marked effect on lifestyle)
-- **5**: dementia, confusion, and/or complete disorientation
-
-#### FATIGUE
-- **0**: none
-- **1**: mild (Does not usually interfere with daily activities)
-- **2**: moderate (Interferes but does not limit daily activities for more than 50%)
-- **3**: severe (Significant limitation in daily activities (> 50% reduction))
-
-**Note**: Because fatigue is difficult to evaluate objectively, in some studies it does not contribute to the Cerebral FS score or EDSS step. Please adhere to the study’s specific
-instructions.
-
-### FUNCTIONAL SYSTEM SCORE
-- **0**: normal
-- **1**: signs only in decrease in mentation; mild fatigue
-- **2**: mild decrease in mentation; moderate or severe fatigue
-- **3**: moderate decrease in mentation
-- **4**: marked decrease in mentation
-- **5**: dementia
-
-### AMBULATION
-
-**Unrestricted Ambulation**
-- The patient can walk a normal distance without assistance, comparable to healthy individuals of similar age and physical condition.
-- EDSS step can range from 0 to 5.0, depending on the Functional System (FS) scores.
-
-**Fully Ambulatory**
-- At least 500 meters of ambulation without assistance, but not unrestricted.
-- EDSS step can range from 2.0 to 5.0, depending on FS scores.
-- The Pyramidal and/or Cerebellar FS must be ≥ 2 to reflect this restriction in ambulation.
-
-**Ambulation < 500 Meters**
-- If the walking distance is less than 500 meters, the EDSS step must be ≥ 4.5, depending on the walking ranges provided by the ambulation score and combination of FS scores.
-- EDSS steps 5.5 to 8.0 are exclusively defined by the ability to ambulate and type of assistance required, or the ability to use a wheelchair.
-
-**Assistance Needed**
-- Definitions for EDSS steps 6.0 or 6.5 include both the type of assistance required when walking and the walking range.
-- Assistance by another person is equivalent to bilateral assistance.
-
-**Note:**
-- The ambulation score represents both the walking range and the type of assistance required.
-- This score replaces several checkboxes used previously on the scoring sheet but does not introduce new definitions.
-- Use of a wheelchair can now be scored on the scoring sheet.
-- Indicate the reported distance and time for the patient in the appropriate field on the scoring sheet, followed by the type of assistance and walking distance measured during assessment.
-
-
-### DISTANCE AND TIME REPORTED BY PATIENT
-
-**Maximal Unassisted Walking Distance**
-- Maximal unassisted walking distance reported by the patient (in meters) without rest or assistance.
-- Time required to walk the maximum distance according to the patient (in minutes).
-
-**Assistance**
-0. Without help or assistance (allowing use of an ankle-foot orthotic device, but no other assistive devices).
-1. Unilateral assistance: one stick/crutch/brace.
-2. Bilateral assistance: two sticks/crutches/braces or assistance by another person.
-3. Wheelchair.
-
-**Distance**
-- Measure the distance the patient can walk in meters.
-  - **Unassisted:** Observe walking for a minimum of 500 meters and measure time needed, if possible.
-  - **Assisted:** Observe walking with assistive devices or help from another person for a minimum of 130 meters, if possible.
-
----
-
-### AMBULATION SCORE
-
-0. Unrestricted
-1. Fully ambulatory
-2. ≥ 300 meters but < 500 meters, without help or assistance (EDSS 4.5 or 5.0)
-3. ≥ 200 meters but < 300 meters, without help or assistance (EDSS 5.0)
-4. ≥ 100 meters but < 200 meters, without help or assistance (EDSS 5.5)
-5. Walking range < 100 meters without assistance (EDSS 6.0)
-6. Unilateral assistance, ≥ 50 meters (EDSS 6.0)
-7. Bilateral assistance, ≥ 120 meters (EDSS 6.0)
-8. Unilateral assistance, < 50 meters (EDSS 6.5)
-9. Bilateral assistance, ≥ 5 meters but < 120 meters (EDSS 6.5)
-10. Uses wheelchair without help; unable to walk 5 meters even with aid, essentially restricted to wheelchair; wheels self and transfers alone; up and about in wheelchair for some 12 hours a day (EDSS 7.0)
-11. Uses wheelchair with help; unable to take more than a few steps; restricted to wheelchair; may need some help in transferring and wheeling self (EDSS 7.5)
-12. Essentially restricted to bed or chair or perambulated in wheelchair, but out of bed most of the day; retains many self-care functions; generally has effective use of arms (EDSS 8.0)
-
-Expanded Disability Status Scale (EDSS)
-
-0   - Normal neurological exam (all Functional Systems [FS] grade 0)
-1.0 - No disability, minimal signs in one FS (one FS grade 1)
-1.5 - No disability, minimal signs in more than one FS (more than one FS grade 1)
-2.0 - Minimal disability in one FS (one FS grade 2, others 0 or 1)
-2.5 - Minimal disability in two FS (two FS grades 2, others 0 or 1)
-3.0 - Moderate disability in one FS (one FS grade 3, others 0 or 1) though fully ambulatory;
-or mild disability in three or four FS (three/four FS grades 2, others 0 or 1) though fully ambulatory
-3.5 - Fully ambulatory but with moderate disability in one FS (one FS grade 3) and mild disability in one or two FS (one/two FS grade 2) and others 0 or 1;
-or fully ambulatory with two FS grades 3 (others 0 or 1);
-or fully ambulatory with five FS grades 2 (others 0 or 1)
-4.0 - Unable to walk > 25 feet without aid
-4.5 - Unable to walk > 100 feet without aid
-5.0 - Relies on a walking aid; unable to walk > 300 feet without resting
-5.5 - Relies on a walking aid; unable to walk > 200 feet without resting
-6.0 - Unable to walk more than 50 feet with or without aid; cannot stand unaided for five minutes
-6.5 - Unable to walk more than 10 feet with or without aid; cannot stand unaided for two minutes
-7.0 - Unable to walk 5 meters even with aid, essentially restricted to wheelchair; wheels self and transfers alone; up and about in wheelchair some 12 hours a day
-7.5 - Unable to take more than a few steps; restricted to wheelchair; may need some help in transferring and in wheeling self
-8.0 - Essentially restricted to bed or chair or perambulated in wheelchair, but out of bed most of the day; retains many self-care functions; generally has effective use of arms
-8.5 - Essentially restricted to bed much of the day; has some effective use of arm(s); retains some self-care functions
-9.0 - Helpless bed patient; can communicate and eat
-9.5 - Totally helpless bed patient; unable to communicate effectively or eat/swallow
-10  - Death due to MS
-
-

prompts/just_edss_schema.gbnf

@@ -1,11 +0,0 @@
-EDSS-kv ::= "\"EDSS\"" space ":" space number
-Reason ::= "\"" char{0,400} "\"" space
-Reason-kv ::= "\"Reason\"" space ":" space Reason
-boolean ::= ("true" | "false") space
-char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-decimal-part ::= [0-9]{1,16}
-integral-part ::= [0] | [1-9] [0-9]{0,15}
-nicht-klassifizierbar-kv ::= "\"nicht_klassifizierbar\"" space ":" space boolean
-number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-root ::= "{" space Reason-kv "," space nicht-klassifizierbar-kv ( "," space ( EDSS-kv ) )? "}" space
-space ::= | " " | "\n"{1,2} [ \t]{0,20}

prompts/just_edss_text.txt

@@ -1,25 +0,0 @@
-Expanded Disability Status Scale (EDSS)
-
-0   - Normal neurological exam (all Functional Systems [FS] grade 0)
-1.0 - No disability, minimal signs in one FS (one FS grade 1)
-1.5 - No disability, minimal signs in more than one FS (more than one FS grade 1)
-2.0 - Minimal disability in one FS (one FS grade 2, others 0 or 1)
-2.5 - Minimal disability in two FS (two FS grades 2, others 0 or 1)
-3.0 - Moderate disability in one FS (one FS grade 3, others 0 or 1) though fully ambulatory;
-or mild disability in three or four FS (three/four FS grades 2, others 0 or 1) though fully ambulatory
-3.5 - Fully ambulatory but with moderate disability in one FS (one FS grade 3) and mild disability in one or two FS (one/two FS grade 2) and others 0 or 1;
-or fully ambulatory with two FS grades 3 (others 0 or 1);
-or fully ambulatory with five FS grades 2 (others 0 or 1)
-4.0 - Unable to walk > 25 feet without aid
-4.5 - Unable to walk > 100 feet without aid
-5.0 - Relies on a walking aid; unable to walk > 300 feet without resting
-5.5 - Relies on a walking aid; unable to walk > 200 feet without resting
-6.0 - Unable to walk more than 50 feet with or without aid; cannot stand unaided for five minutes
-6.5 - Unable to walk more than 10 feet with or without aid; cannot stand unaided for two minutes
-7.0 - Unable to walk 5 meters even with aid, essentially restricted to wheelchair; wheels self and transfers alone; up and about in wheelchair some 12 hours a day
-7.5 - Unable to take more than a few steps; restricted to wheelchair; may need some help in transferring and in wheeling self
-8.0 - Essentially restricted to bed or chair or perambulated in wheelchair, but out of bed most of the day; retains many self-care functions; generally has effective use of arms
-8.5 - Essentially restricted to bed much of the day; has some effective use of arm(s); retains some self-care functions
-9.0 - Helpless bed patient; can communicate and eat
-9.5 - Totally helpless bed patient; unable to communicate effectively or eat/swallow
-10  - Death due to MS