FremyCompany/women_health_10k_insights

Women's Health 10k Insights

Women's Health 10k Insights is a dataset of medical cases selected for their relevance to women's health and processed into concise, practice-oriented tips for clinicians and medical AI agents.

The dataset is intended to help surface uncommon presentations, diagnostic pitfalls, misleading test results, and other lessons that may reduce avoidable clinical mistakes. It is an educational and decision-support resource—not a diagnostic system or a substitute for clinical judgment.

Dataset overview

The dataset was created through a multi-stage terminology and case-filtering pipeline:

1. 1.Approximately 770,000 medical terms were downloaded from SNOMED CT.
2. 2.16 Codex subagents reviewed and filtered the terminology for relevance to women's health, with particular attention to hormonal health.
3. 3.The resulting vocabulary was expanded, normalized, and filtered using Codex and Python, producing a curated list of approximately 3,000 terms.
4. 4.This terminology was used to score approximately 250,000 medical cases from PMC-Patients for women's-health relevance.
5. 5.The 10,000 highest-relevance cases were retained.
6. 6.The selected cases were processed using the ChatGPT API to produce concise clinical tips and insights designed for practical use by clinicians and medical agents.

SNOMED CT              Curated vocabulary       PMC-Patients
770k terms  ────────►  3k terms  ────────────►  250k cases
     │                                           │
     └─ 16 Codex subagents                       └─ relevance scoring
        + Codex/Python                              ↓
                                                10k cases
                                                    │
                                                    └─ ChatGPT API
                                                       ↓
                                                clinical insights

Example insights

If an adolescent has progressive pelvic pain and a pelvic mass despite apparently regular, painless menstruation and an intact hymen, remember that one hemivagina may be obstructed while the other menstruates normally. Look for the combination of uterus didelphys, an obstructed hemivagina with hematocolpos/hematosalpinx, and ipsilateral renal agenesis on MRI—findings characteristic of Herlyn-Werner-Wunderlich syndrome. This matters because precise imaging can guide a hymen-sparing vaginoscopic incision of the vaginal septum, allowing evacuation of retained blood and preservation of the cervix and hymen.

If a hormone result is dramatically discordant with the patient's clinical state—such as persistently high estradiol despite bilateral oophorectomy, menopausal symptoms, and appropriately elevated FSH—suspect assay interference before pursuing an endocrine tumor. Confirm the result using a different assay platform and, when available, heterophile-blocking treatment. The practical lesson is to treat laboratory values as measurements requiring clinical validation, not as diagnoses in isolation.

If a presumed Bartholin cyst causes recurrent, menstruation-linked vulvar pain, do not assume it is a routine cyst: consider ectopic endometriosis and send the cyst wall or contents for pathological examination. The clue is the cyclical symptoms plus chocolate-colored fluid, reflecting repeated hemorrhage from endometrial tissue. Pelvic imaging may be unrevealing, so the menstrual pattern and operative findings should guide suspicion even when ultrasound or MRI does not clearly demonstrate disease.

Intended uses

Potential uses include:

• —Retrieval-augmented generation for medical agents evaluating a case.
• —Surfacing relevant diagnostic pitfalls and uncommon presentations at the point of care.
• —Educational tools for clinicians, trainees, and medical researchers.
• —Research on retrieval, ranking, and summarization of case-derived clinical knowledge.
• —Evaluation of whether a clinical assistant can retrieve and apply relevant lessons without overstating the evidence.

Out-of-scope uses

This dataset should not be used:

• —As a standalone diagnostic, treatment, triage, or prescribing system.
• —To replace examination, patient history, laboratory validation, imaging, specialist consultation, or local clinical guidelines.
• —As the sole basis for decisions affecting an individual patient's care.
• —To infer that a condition is exclusive to women merely because it appears in this collection.
• —To train or deploy systems without appropriate evaluation, clinician oversight, and safeguards.

Data provenance

The source case collection is PMC-Patients, which was derived from case reports available through PubMed Central. SNOMED CT terminology was used during vocabulary construction and relevance filtering; the released insights are generated summaries rather than a redistribution of the complete SNOMED CT terminology release.

Users are responsible for reviewing the source datasets, source publications, and applicable licenses or terms before redistribution or commercial use. The cc0-1.0 declaration above applies to this dataset release and should not be interpreted as relicensing third-party source material.

Limitations and known risks

• —Generated content: The insights were produced with a language model and may contain omissions, overgeneralizations, unsupported specificity, or factual errors.
• —Case-report bias: Published case reports disproportionately describe rare, unusual, severe, or diagnostically challenging presentations. They do not represent population prevalence or routine clinical practice.
• —Selection bias: Relevance scoring depends on the curated terminology, filtering process, and model judgments. Relevant cases may have been excluded, and marginal cases may have been included.
• —Evidence quality: A lesson derived from an individual case report is not equivalent to a clinical guideline, systematic review, or validated standard of care.
• —Temporal limitations: Medical knowledge, terminology, guidelines, and recommended management change over time.
• —Population coverage: Women's health is broader than anatomy, reproductive health, and hormonal health. The dataset may underrepresent transgender, non-binary, intersex, post-reproductive, and other patient populations.
• —Automation risk: Retrieval of a relevant tip does not establish that the tip applies to the patient under evaluation.

All outputs should be checked against the underlying case, current clinical guidance, and the patient's circumstances before use.

Responsible use

Systems built with this dataset should:

1. 1.Present retrieved insights as considerations, not conclusions.
2. 2.Preserve links or identifiers to source evidence whenever available.
3. 3.Distinguish source text from model-generated interpretation.
4. 4.Communicate uncertainty and provide alternative explanations.
5. 5.Avoid making autonomous clinical decisions.
6. 6.Be evaluated by qualified clinicians, including testing for harmful omissions and subgroup performance.
7. 7.Protect patient privacy and avoid attempts to re-identify individuals described in source reports.

Validation recommendations

Before deployment, developers should evaluate at least:

• —Clinical accuracy and faithfulness to the source case.
• —Relevance of retrieved insights to the presenting case.
• —Citation or provenance correctness.
• —Rate and severity of unsupported claims.
• —Performance across specialties, ages, pregnancy status, hormonal states, and patient populations.
• —Whether the system appropriately abstains when evidence is weak or irrelevant.

Citation

If you use this dataset, please cite this Hugging Face dataset repository.

Acknowledgements

This dataset builds on medical terminology from SNOMED CT and cases from PMC-Patients. The filtering and insight-generation pipeline used Codex (GPT 5.6 Sol main, Luna subagents), Python, and the ChatGPT API.

Disclaimer

This dataset is provided for research and educational purposes. No warranty is made regarding completeness, accuracy, clinical validity, or fitness for a particular purpose. Clinical decisions must be made by appropriately qualified professionals using current evidence, applicable regulations, and the full context of the individual patient.