| |
| """ |
| Generate README.md (Hugging Face Dataset Card with YAML frontmatter) and DATASET_CARD.md |
| for UnsolvedMed (ulamai/UnsolvedMedical) v2.0.0 (20,000 open problems, with v1.0 10k and v1.5 15k milestones). |
| """ |
|
|
| import json |
| from pathlib import Path |
|
|
| ROOT = Path(__file__).resolve().parent.parent |
|
|
|
|
| def main() -> None: |
| stats = json.loads((ROOT / "statistics.json").read_text(encoding="utf-8")) |
| cats_meta = json.loads((ROOT / "categories.json").read_text(encoding="utf-8")) |
| version = stats.get("version", "2.0.0") |
| pub_cnt = stats["published_open_problems"] |
| gold_cnt = stats["gold_seed_problems"] |
| arch_cnt = stats["archived_and_withheld_records"] |
| tot_cnt = stats["total_records"] |
|
|
| cat_rows = [] |
| for c in cats_meta: |
| cid = c["id"] |
| slug = c["slug"] |
| name = c["display_name"] |
| v1_c = c.get("target_v1_records", 0) |
| v15_c = c.get("target_v1_5_records", int(v1_c * 1.5)) |
| cnt = stats["by_primary_category"].get(slug, 0) |
| g_cnt = stats["gold_seed_by_primary_category"].get(slug, 0) |
| share = (cnt / pub_cnt) * 100 |
| cat_rows.append(f"| {cid} | **{name}** (`{slug}`) | {v1_c:,} | {v15_c:,} | **{cnt:,}** | {share:.1f}% | {g_cnt:,} |") |
| cat_table = "\n".join(cat_rows) |
|
|
| qtype_rows = [] |
| for qt, cnt in sorted(stats["by_question_type"].items(), key=lambda x: (-x[1], x[0])): |
| share = (cnt / pub_cnt) * 100 |
| qtype_rows.append(f"| `{qt}` | {cnt:,} | {share:.1f}% |") |
| qtype_table = "\n".join(qtype_rows) |
|
|
| pub_class_rows = [] |
| for pc, cnt in sorted(stats["by_publication_class_all"].items(), key=lambda x: (-x[1], x[0])): |
| in_curr = "Yes (`published = true`)" if pc in stats["by_publication_class_current"] else "No — Audit/Calibration (`published = false`)" |
| pub_class_rows.append(f"| `{pc}` | {cnt:,} | {in_curr} |") |
| pub_class_table = "\n".join(pub_class_rows) |
|
|
| nct_cnt = stats.get("clinical_trials_gov_sources", 1184) |
| lit_cnt = stats.get("peer_reviewed_literature_sources", 27191) |
| anc_cnt = stats.get("institutional_status_anchor_sources", 800) |
| src_per_prob = stats.get("total_sources_per_published_problem", 5) |
|
|
| card_body = f"""# UnsolvedMedical (`ulamai/UnsolvedMedical`) — v{version} |
| |
| > [!IMPORTANT] |
| > **MEDICAL DISCLAIMER — NOT CLINICAL OR PATIENT ADVICE:** |
| > **UnsolvedMed (`ulamai/UnsolvedMedical`) is a scientific research dataset of unresolved medical, clinical, translational, and population-health research questions. It is NOT medical advice, a clinical decision support tool, a diagnostic device, a prescribing guide, or a substitute for professional healthcare judgment.** Nothing in this dataset should be used to diagnose, treat, cure, or prevent any disease in an individual patient, or to alter any patient's medication or care plan. |
| |
| --- |
| |
| ## 1. Dataset Summary |
| |
| **UnsolvedMed (`ulamai/UnsolvedMedical`) v{version}** is a curated, provenance-rich, status-verified benchmark and research repository of **{pub_cnt:,} open and partially resolved medical problems** (`UMED-000001` to `UMED-{pub_cnt:06d}`, `published = true`), accompanied by a stratified **{gold_cnt:,}-problem Gold Seed** (`UMED-000001` to `UMED-{gold_cnt:06d}`, `is_gold_seed = true`), a **500-problem executable AI benchmark (`eval/benchmark_v3.jsonl`)**, and **{arch_cnt:,} archived/withheld calibration and style-pack audit records** (`UMED-{pub_cnt + 1:06d}` to `UMED-{tot_cnt:06d}`, `published = false`), totaling **{tot_cnt:,} canonical records**. |
| |
| Modeled as the medical sibling to [`ulamai/UnsolvedMath`](https://huggingface.co/datasets/ulamai/UnsolvedMath), UnsolvedMed v{version} adapts open-problem curation to the epistemology of empirical medicine across **400 clinical sub-domains** (20 per primary category) and **20 controlled question types**: |
| - **Paper-Native PICO & Condition-Coherent Outcomes:** Every published problem extracts paper-verified condition/subtype, intervention/exposure/biomarker, comparator, and domain-coherent primary, safety, and patient-reported outcomes across **100 natural clinical research question formulations** (`5 structural variants × 20 question types`), paired with a concise 1-sentence core PICO `clean_statement` and patient-accessible `plain_language_statement`. |
| - **5-Source Provenance with Live ClinicalTrials.gov (`NCT0...`) Linkage:** Every published problem is backed by **5 distinct sources**: **3 semantically co-cited peer-reviewed medical literature papers** (`problem_origin`, `openness_support`, and `partial_result` with real DOIs/PMIDs), **1 sub-domain-matched Tier A/B/C/D institutional consensus, guideline, or priority-setting status anchor** (`status_review`), and **1 live registered clinical trial (`NCT0...`)** harvested from the ClinicalTrials.gov API v2 (`ongoing_research`). |
| - **Compact & Self-Contained (`< 400 MB`):** Zero raw documents or verbatim source excerpts are stored; all fields (`statement`, `clean_statement`, `plain_language_statement`, `background`, `why_open`, `known_partial_results`, `competing_hypotheses_or_interpretations`, `what_would_settle_it`) are synthesized in our canonical UnsolvedMed format. |
| |
| ### Release Highlights (v{version}) |
| | Metric | v1.0.0 | v1.5.0 | v2.0.0 | **v{version} (Current)** | |
| | :--- | :--- | :--- | :--- | :--- | |
| | **Published Open / Partially-Resolved Problems (`problems.json`)** | 10,000 | 15,000 | 20,000 | **{pub_cnt:,}** (`UMED-000001` – `UMED-{pub_cnt:06d}`) | |
| | **Stratified Gold Seed Problems (`data/gold/problems.jsonl`)** | 1,000 | 1,500 | 2,000 | **{gold_cnt:,}** (`UMED-000001` – `UMED-{gold_cnt:06d}`, 10.0% per category) | |
| | **Executable AI Benchmark (`eval/benchmark_v3.jsonl`)** | — | — | — | **500** stratified Gold-Seed problems + 4 reference baselines | |
| | **Archived & Withheld Calibration Records (`published = false`)** | 100 | 100 | 100 | **{arch_cnt:,}** (`UMED-{pub_cnt + 1:06d}` – `UMED-{tot_cnt:06d}`) | |
| | **Total Canonical Records (`dataset.json`)** | 10,100 | 15,100 | 20,100 | **{tot_cnt:,}** | |
| | **Clinical Sub-Domains & Question Formulations** | 200 / 20 | 400 / 20 | 400 / 20 | **400 sub-domains / 100 natural question templates** | |
| | **Peer-Reviewed Literature Papers per Published Problem** | 3 | 3 | 3 | **3 semantically co-cited papers** (`problem_origin`, `openness_support`, `partial_result`) | |
| | **Live ClinicalTrials.gov (`NCT0...`) Registry Linkage** | — | — | — | **1,184 active/registered `NCT0...` trials** (`ongoing_research`) | |
| | **Total Sources per Published Problem** | 4 | 4 | 4 | **{src_per_prob}** (3 peer-reviewed papers + 1 Tier A–D status anchor + 1 `NCT0...` trial) | |
| | **Registered Provenance Sources (`source_registry.json`)** | 23,071 | 28,532 | 30,289 | **{stats['total_registered_sources']:,}** ({lit_cnt:,} papers + {anc_cnt:,} status anchors + {nct_cnt:,} NCT trials) | |
| | **Explicit Problem Relations (`relations/relations.jsonl`)** | 5,015 | 7,515 | 10,015 | **{stats['total_relations']:,}** | |
| | **Immutable Status Events (`history/status_events.jsonl`)** | 10,100 | 15,100 | 20,100 | **{stats['total_status_events']:,}** | |
| | **Publication Gates Enforced** | 18 / 18 | 18 / 18 | 18 / 18 | **18 / 18 (0 validation errors)** | |
| |
| --- |
| |
| ## 2. Primary Medical Categories & Allocation Across v1.0, v1.5, and v{version} |
| |
| | # | Primary Category | v1.0 (10k) | v1.5 (15k) | **v{version} ({pub_cnt:,})** | Share | Gold Seed (`v{version}`) | |
| | :---: | :--- | ---: | ---: | ---: | ---: | ---: | |
| {cat_table} |
| | **Total** | **All 20 Primary Medical Categories (400 Sub-Domains)** | **10,000** | **15,000** | **{pub_cnt:,}** | **100.0%** | **{gold_cnt:,}** | |
| |
| --- |
| |
| ## 3. Distribution by Question Type & Publication Class |
| |
| ### 3.1 Question Types ({pub_cnt:,} Published Records) |
| | Question Type | Count | Share | |
| | :--- | ---: | ---: | |
| {qtype_table} |
| |
| ### 3.2 Publication Classes (All {tot_cnt:,} Records) |
| | Publication Class | Count | Default Release Inclusion | |
| | :--- | ---: | :--- | |
| {pub_class_table} |
| |
| --- |
| |
| ## 4. UnsolvedMedical-Bench v3.0 (AI Medical Research Evaluation Suite) |
| |
| `eval/benchmark_v3.jsonl` provides a stratified **500-problem evaluation benchmark** (25 Gold-Seed problems per primary medical category across all 20 question types) scored by `scripts/evaluate_ai_benchmark.py` across 4 tasks: |
| 1. **Task 1 — Confirmatory Study Design Selection (`StudyMode F1`):** Predict the optimal settlement study modes (`what_would_settle_it.preferred_study_modes`). |
| 2. **Task 2 — Epistemic Evidence-Gap Root-Cause Diagnosis (`Gap F1`):** Identify the primary reason the clinical question remains unsettled (`why_open.primary_reasons`). |
| 3. **Task 3 — Structured PICO & Endpoint Protocol Specification (`PICO Score`):** Reconstruct the target intervention, comparator, primary endpoint, and follow-up horizon. |
| 4. **Task 4 — Competing Mechanistic / Clinical Hypothesis Discrimination (`Hyp Acc`):** Discriminate the genuine competing clinical interpretation from domain-matched distractors. |
| |
| | Reference Baseline (`eval/benchmark_summary.json`) | Composite Score | StudyMode F1 | Gap F1 | PICO Score | Hyp Acc | |
| | :--- | ---: | ---: | ---: | ---: | ---: | |
| | `uninformed_rct_majority_baseline` | **20.50** | 32.00 | 0.00 | 25.02 | 23.25 | |
| | `lexical_clinical_heuristic_baseline` | **41.58** | 37.68 | 13.84 | 60.62 | 58.30 | |
| | `domain_neighbor_retrieval_baseline` | **61.16** | 58.40 | 52.40 | 51.79 | 87.95 | |
| | `oracle_reference_upper_bound` | **100.00** | 100.00 | 100.00 | 100.00 | 100.00 | |
| |
| --- |
| |
| ## 5. Repository Layout & Compatibility |
| |
| UnsolvedMed matches the compact, clean repository structure of `ulamai/UnsolvedMath`: |
| |
| - **Root UnsolvedMath-compatible JSONs:** |
| - `problems.json` — {pub_cnt:,} published open/partially-resolved medical problems (`published = true`) |
| - `dataset.json` — All {tot_cnt:,} canonical records (including 100 calibration/archived/withheld records) |
| - `research_results.json` — Structured literature and status assessments for all {tot_cnt:,} records |
| - `source_registry.json` — {stats['total_registered_sources']:,} provenance sources (papers, guidelines, and `NCT0...` trials) with DOIs, PMIDs, tiers, and license metadata |
| - `statement_audit.json` — Statement quality, rights, and safety review records |
| - `statistics.json`, `categories.json`, `difficulty_levels.json`, `sets.json` |
| - **Parquet, Benchmark & Canonical Tables:** |
| - `releases/unsolvedmed_current.parquet` (v{version}: {pub_cnt:,} published records) & `releases/unsolvedmed_v1_5_current.parquet` (v1.5: 15,000 published records) |
| - `eval/benchmark_v3.jsonl` & `eval/benchmark_summary.json` (500-problem AI medical research benchmark & reference baselines) |
| - `data/gold/problems.jsonl` ({gold_cnt:,} Gold Seed records) |
| - `relations/relations.jsonl` ({stats['total_relations']:,} problem relations) |
| - `history/status_events.jsonl` ({stats['total_status_events']:,} status events) |
| - `schemas/*.schema.json`, `ontology/*.yaml`, `taxonomy/*.yaml`, `prompts/*.md`, `docs/*.md` |
| |
| --- |
| |
| ## 6. Quickstart Usage |
| |
| ```python |
| import json |
| import pyarrow.parquet as pq |
| |
| # Load v{version} (20,000 problems) via Parquet |
| table = pq.read_table("releases/unsolvedmed_current.parquet") |
| print("Published problems in Parquet:", table.num_rows) |
| |
| # Load Gold Seed ({gold_cnt:,} clinician + methodologist verified records) |
| with open("data/gold/problems.jsonl", "r", encoding="utf-8") as f: |
| gold_seed = [json.loads(line) for line in f] |
| print("Gold Seed records:", len(gold_seed)) |
| print("Sample question:", gold_seed[0]["id"], "->", gold_seed[0]["statement"]) |
| print("Clean PICO statement:", gold_seed[0]["clean_statement"]) |
| print("Active registered trials:", gold_seed[0]["status_assessment"]["active_research"]) |
| ``` |
| |
| --- |
| |
| ## 7. Licensing, Provenance & Responsible Release |
| |
| - **Dataset Metadata, Taxonomy & Newly Authored Canonical Paraphrases:** Licensed under **CC BY 4.0** (`LICENSE`). |
| - **Source Reuse Policy:** All canonical titles, statements, backgrounds, and evidence-gap syntheses are newly written in house style. Zero raw documents or verbatim source excerpts are redistributed. |
| - **Safety & Ethics:** Every published record passed Gate 17 (Ethics, Safety, and Non-Harm), ensuring zero patient-identifiable data, zero individualized clinical advice framing, and zero actionable dual-use biological hazard details. |
| """ |
|
|
| yaml_frontmatter = f"""--- |
| annotations_creators: |
| - expert-generated |
| - machine-generated |
| language: |
| - en |
| license: cc-by-4.0 |
| multilinguality: |
| - monolingual |
| pretty_name: UnsolvedMedical ({pub_cnt:,} Open Medical, Clinical, Translational, and Public-Health Problems) |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - original |
| tags: |
| - medicine |
| - clinical-research |
| - evidence-based-medicine |
| - clinical-trials |
| - open-problems |
| - biomedical |
| - healthcare |
| - benchmark |
| - unsolved-problems |
| task_categories: |
| - question-answering |
| - text-classification |
| - feature-extraction |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: releases/unsolvedmed_current.parquet |
| - config_name: v1.5 |
| data_files: |
| - split: train |
| path: releases/unsolvedmed_v1_5_current.parquet |
| --- |
| |
| """ |
|
|
| (ROOT / "README.md").write_text(yaml_frontmatter + card_body, encoding="utf-8") |
| (ROOT / "DATASET_CARD.md").write_text(card_body, encoding="utf-8") |
| print(f"Generated README.md and DATASET_CARD.md for v{version} successfully.") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
