Interobserver Reproducibility of Two Endometriosis Scoring Systems: A Multicentre Observational Prospective Study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to assess the interobserver reproducibility of the Revised American Society for Reproductive Medicine score (r-ASRM) and the Endometriosis Fertility Index (EFI) in women undergoing a conservative laparoscopy to treat endometriosis. METHODS: The r-ASRM stage and score and EFI were independently determined by 2 assessors participating in the surgery. Assessors were either a minimally invasive specialist or fellow, a fertility specialist, or an obstetrics and gynecology resident. They both completed the score sheets separately, blindly to the other assessor. A consensus was then obtained, after discussion between the 2 assessors. Interobserver reproducibility was evaluated using Cohen's κ and intraclass correlation coefficient for scores by categories and continuous scores, respectively. RESULTS: In this multicentre cross-sectional cohort study, 100 women undergoing a laparoscopy for endometriosis were recruited between April 2020 and May 2023. Most participants had stage 3 (22%) or 4 (43%) endometriosis. The interobserver agreement was strong for the r-ASRM stage, and almost perfect for EFI categories (0-3, 4, 5, 6, 7-8, 9-10) and Least function score categories (1-3, 4-6, and 7-8). The agreement for the r-ASRM score and EFI score is excellent. Interobserver agreement remained strong, regardless of the level of expertise, the use of preoperative suppression, or history of a pelvic surgery. CONCLUSIONS: The r-ASRM and EFI classifications are highly reproductible between assessors, making them excellent tools for communication between health professionals. However both are limited by their capacity to explain variations in pain symptoms, which remains a challenge to be addressed in future work.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".