Inter‐observer reproducibility of the 2021 <scp>AAGL</scp> Endometriosis Classification
Bibliographic record
Abstract
BACKGROUND: Inter-observer agreement for the American Association of Gynecologic Laparoscopists (AAGL) 2021 Endometriosis Classification staging system has not been described. Its predecessor staging system, the revised American Society for Reproductive Medicine (rASRM), has historically demonstrated poor inter-observer agreement. AIMS: We aimed to determine the inter-observer agreement performance of the AAGL 2021 Endometriosis Classification staging system, and compare this with the rASRM staging system. MATERIALS AND METHODS: A database of 317 patients with coded surgical data was retrospectively analysed. Three independent observers allocated AAGL surgical stages (1-4), twice. Observers made their own interpretation of how to apply the tool in the first staging allocation. Consensus rules were then developed for a second staging allocation. RESULTS: First staging allocation: odds ratio (OR) (and 95% CI) for observer 1 to score higher than observer 2 was 8.08 (5.12-12.76). Observer 1 to score higher than observer 3 was 12.98 (7.99-21.11) and observer 2 to score higher than observer 3 was 1.61 (1.03-2.51). This represents poor agreement. Second staging allocation (after consensus): OR for observer 1 to score higher than observer 2 was 1.14 (0.64-2.03), observer 1 to score higher than observer 3 was 1.81 (0.99-3.28) and observer 2 to score higher than observer 3 was 1.59 (0.87-2.89). This represents good agreement. CONCLUSIONS: These findings suggest that in its current format the AAGL 2021 Endometriosis Classification staging system has poor inter-observer agreement, not superior to the rASRM staging system. However, performance improved when additional measures were taken to simplify and clarify areas of ambiguity in interpreting the staging system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".