Claw Semantic Sim

• Panel A: Semantic isolation by disease category (boxplot)
• Panel B: Top 20 most semantically isolated diseases (bar chart, NTD/Global South colour-coded)
• Panel C: Semantic isolation vs research volume (scatter with regression)
• Panel D: NTD vs non-NTD significance test (Welch's t-test, Cohen's d)

Why this exists

If you ask ChatGPT to "measure research neglect for diseases," it will:

• Not know which embedding model to use for biomedical text
• Hallucinate metrics that sound plausible but have no methodological grounding
• Skip quality filtering (year coverage, abstract coverage, minimum papers)
• Not handle MPS acceleration or checkpointed batch processing
• Produce a single scatter plot with no disease classification

This skill encodes the correct methodological decisions:

• Uses PubMedBERT (the gold-standard biomedical language model)
• Fetches from PubMed with exponential backoff and NCBI rate limiting
• Quality filters: year coverage >= 70%, abstract coverage >= 95%, minimum 50 papers
• Batch embedding with Apple MPS acceleration and CPU fallback
• Checkpointed processing (resume after interruption)
• HDF5 storage with gzip compression and SHA-256 checksums
• Classification against WHO NTD list and Global South priority diseases
• Statistical significance testing (Welch's t-test, Cohen's d)

Key Finding

Neglected tropical diseases (NTDs) are significantly more semantically isolated than other conditions (P < 0.001, Cohen's d = 0.8+). They exist in knowledge silos with limited cross-disciplinary research bridges. The 25 most isolated diseases are disproportionately Global South priority conditions.

Pipeline

05-00-heim-sem-setup.py     # Validate environment, create directories
05-01-heim-sem-fetch.py     # Retrieve PubMed abstracts (checkpointed)
05-02-heim-sem-embed.py     # Generate PubMedBERT embeddings (MPS/CPU)
05-03-heim-sem-compute.py   # Compute SII, KTP, RCC, temporal drift
05-04-heim-sem-figures.py   # Generate publication figures
05-05-heim-sem-integrate.py # Merge with biobank + clinical trial dimensions

Demo (works out of the box)

python semantic_sim.py --demo --output demo_report

The demo uses pre-computed embeddings and metrics for 175 GBD diseases and generates the full 4-panel figure instantly.

Example Output

Semantic Similarity Index
=========================
Diseases analysed: 175
Total PubMed abstracts: 13,100,000
Embedding model: PubMedBERT (768-dim)

Metric Ranges:
  SII: 0.0412 - 0.1893
  KTP: 0.6234 - 0.9187
  RCC: 0.0891 - 0.3421

Key Finding:
  NTDs show +38% higher semantic isolation
  P < 0.0001, Cohen's d = 0.84
  14/25 most isolated diseases are Global South priority

Figures saved to: demo_report/
  Fig5_Semantic_Structure.png (300 dpi)
  Fig5_Semantic_Structure.pdf (vector)

Reproducibility:
  commands.sh | environment.yml | checksums.sha256

Interpretation Guide

• High SII: Disease research exists in a knowledge silo; limited cross-disciplinary bridges
• Low KTP: Research on this disease has few methodological overlaps with others
• High RCC: Diverse research approaches within the disease (many subtopics)
• High Temporal Drift: Research focus has shifted significantly over time
• NTDs shown in red, Global South diseases in orange, others in grey
• The scatter plot (Panel C) reveals the inverse relationship between research volume and isolation

Citation

If you use this skill in a publication, please cite:

• Corpas, M. et al. (2026). HEIM: Health Equity Index for Measuring structural bias in biomedical research. Under review.
• Corpas, M. (2026). ClawBio. https://github.com/ClawBio/ClawBio