Diagnostic accuracy of a novel endometriosis staging system: an external validation study
Bibliographic record
Abstract
Objective To externally validate the “2021 AAGL Endometriosis Classification” staging system. Design Retrospective, diagnostic accuracy study Setting Multicentre Population or Sample Two hundred and seventy-two endometriosis patients (January 2016 - October 2021) Methods Three independent observers analysed coded surgical data to assign an AAGL surgical stage (1 to 4) as the index test, and surgical complexity level (A to D) as the reference standard. Main Outcome Measures The diagnostic accuracy of each AAGL stage to predict corresponding AAGL surgical complexity level was determined. Receiver operating characteristic curves used to determine the accuracy of cut off points used in the AAGL staging system to discriminate between surgical complexity levels. Results 272 cases were analysed. Diagnostic accuracy (sensitivity, specificity, PPV and NPV) for three observers were: stage 1 to predict level A 97.9-98.7%, 60.2-64.2%, 75.0-76.9%, and 96.3-97.5%; stage 2 to predict level B 26.1-30.4%, 93.2-95.6%, 26.3-35.3%, and 92.9-93.6%; stage 3 to predict level C 7.5-10.0%, 93.8-94.8%, 33.3-42.1%, and 70.9-71.5%; stage 4 to predict level D 90.-95.0%, 90.1-91.7% &, 41.9-47.5%, and 99.1-99.6%. For three observers AUROC for A vs B/C/D (cut-point 9) 0.75-0.88, A/B vs C/D (cut-point 16) 0.81 and A/B/C vs D (cut-point 22) 0.95-0.96. Conclusions This external validation study demonstrates that the AAGL Endometriosis Classification performs poorly overall for the prediction of surgical complexity. The results from this external validation study suggest that the system in its current form is not generalizable to all endometriosis patients and should be reviewed before its universal implementation. Funding Nil Keywords Endometriosis, staging, laparoscopy
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".