Clinical diagnosis of endometriosis: a call to action
Bibliographic record
Abstract
Endometriosis can have a profound impact on women's lives, including associated pain, infertility, decreased quality of life, and interference with daily life, relationships, and livelihood. The first step in alleviating these adverse sequelae is to diagnose the underlying condition. For many women, the journey to endometriosis diagnosis is long and fraught with barriers and misdiagnoses. Inherent challenges include a gold standard based on an invasive surgical procedure (laparoscopy) and diverse symptomatology, contributing to the well-established delay of 4-11 years from first symptom onset to surgical diagnosis. We believe that remedying the diagnostic delay requires increased patient education and timely referral to a women's healthcare provider and a shift in physician approach to the disorder. Endometriosis should be approached as a chronic, systemic, inflammatory, and heterogeneous disease that presents with symptoms of pelvic pain and/or infertility, rather than focusing primarily on surgical findings and pelvic lesions. Using this approach, symptoms, signs, and clinical findings of endometriosis are anticipated to become the main drivers of clinical diagnosis and earlier intervention. Combining these factors into a practical algorithm is expected to simplify endometriosis diagnosis and make the process accessible to more clinicians and patients, culminating in earlier effective management. The time has come to bridge disparities and to minimize delays in endometriosis diagnosis and treatment for the benefit of women worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.085 | 0.157 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.020 | 0.040 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.061 | 0.080 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".