IIB or not IIB, part 1: retrospective evaluation of Kenney–Doig categorization of equine endometrial biopsies at a veterinary diagnostic laboratory and comparison with published reports
Bibliographic record
Abstract
The Kenney-Doig scale is a histopathology categorization (grading) system often used as the standard for assessing endometrial disease and communicating prognostic fertility information for equine breeding prospects. We investigated how Kenney-Doig categories compared within the same institution and across different institutions to determine if observer variability may contribute to category frequencies. We conducted a retrospective analysis of all equine endometrial submission records between 1998 and 2018 at the Western College of Veterinary Medicine (WCVM) and Prairie Diagnostic Services (PDS). Of 726 biopsies, we found the following category distribution: 46 of 726 (6.3%) I, 307 of 726 (42.3%) IIA, 326 of 726 (44.9%) IIB, and 47 of 726 (6.5%) III. We also conducted a review of the literature and included 6 studies reporting Kenney-Doig category distributions. Chi-square analysis showed significant differences between the category distribution found at WCVM and PDS and the category distribution reported in the 6 studies. To account for differences in mare populations, individual category distributions were generated for 5 pathologists at the WCVM and PDS. The Fisher exact test among these 5 Kenney-Doig categories revealed significant differences in category tendencies, suggesting that observer variation affects the use of the scale. Our results suggest that there is a need for prospective inter-rater and intra-rater agreement studies of the repeatability of the Kenney-Doig scale.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".