IIB or not IIB, part 2: assessing inter-rater and intra-rater repeatability of the Kenney–Doig scale in equine endometrial biopsy evaluation
Bibliographic record
Abstract
Inter- and intra-rater variability negatively affects the reliability of various histopathology grading scales used as prognostic aids in human and veterinary medicine. The Kenney-Doig categorization (grading) scale, which is used to associate equine endometrial histologic lesions with prognostic estimation of a broodmare's reproductive potential, has not been evaluated for inter- or intra-rater variability, to our knowledge. To assess whether the Kenney-Doig system produces reliable results among observers, 8 pathologists, all with American College of Veterinary Pathologists certification, were recruited to blindly categorize the same set of 63 digital equine endometrial biopsy slides as well as to re-evaluate anonymously 21 of 63 of these slides at a later time. Cohen kappa values for pairwise comparison of final Kenney-Doig categories were -0.05 to 0.46 (unweighted) and 0.08-0.64 (weighted), with an average Light kappa of 0.19 (unweighted) and 0.36 (weighted) across all 8 pathologists, 0.14 (unweighted) and 0.33 (weighted) for pathologists at different institutions, and 0.22 (unweighted) and 0.46 (weighted) for pathologists at the same institution. Intra-class correlations measuring intra-rater agreement were 0.12-0.77 with an average of 0.55 for all 8 pathologists. We found that only slight-to-moderate inter-rater agreement and poor-to-good intra-rater agreement was produced by 8 pathologists using the Kenney-Doig scale, suggesting that the system is subject to significant observer variability and care should be taken when communicating Kenney-Doig categories to submitting clinicians with emphasis on the quality of endometrial lesions present instead of the category and associated expected foaling rate.
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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 | 0.062 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".