World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonization Project: V. Physical examination standards in endometriosis research
Bibliographic record
Abstract
OBJECTIVE: The World Endometriosis Research Foundation established the Endometriosis Phenome and Biobanking Harmonisation Project (EPHect) to create standardized documentation tools (with common data elements) to facilitate the comparison and combination of data across different research sites and studies. In 2014, 4 data research standards were published: clinician-reported surgical data, patient-reported clinical data, and fluid and tissue biospecimen collection. Our current objective is to create an EPHect standard for the clinician-reported physical examination (EPHect-PE) for research studies. DESIGN: An international consortium involving 26 clinical and academic experts and patient partners from 11 countries representing 25 institutions and organizations. Two virtual workshops, followed by the development of the physical examination standards underwent multiple rounds of iterations and revisions. SUBJECTS: N/A MAIN OUTCOME MEASURE(S): N/A RESULT(S): The EPHect-PE tool provides standardized assessment of physical examination characteristics and pain phenotyping. Data elements involve examination of back and pelvic girdle; abdomen including allodynia and trigger points; vulva including provoked vestibulodynia; pelvic floor muscle tone and tenderness; tenderness on unidigital pelvic examination; presence of pelvic nodularity; uterine size and mobility; presence of adnexal masses; presence of incisional masses; speculum examination; tenderness and allodynia at an extra-pelvic site (e.g., forearm); and recording of anthropometrics. CONCLUSION(S): The EPHect-PE standards will facilitate the standardized documentation of the physical examination, including the assessment and documentation of examination phenotyping of endometriosis-associated pelvic pain.
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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 | 0.294 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".