Effect of Tissue Processing on Assessment of Endoscopic Intestinal Biopsies in Dogs and Cats
Bibliographic record
Abstract
BACKGROUND: Prior studies failed to detect significant association between hypoalbuminemia and small intestinal lesions. HYPOTHESIS: Use of pictorial templates will enhance consistency of interpathologist interpretation and identification of intestinal lesions associated with hypoalbuminemia. ANIMALS: Tissues from 62 dogs and 25 cats examined as clinical cases at 7 referral veterinary practices in 4 countries. METHODS: Retrospective, observational study. Histopathology slides from sequential cases undergoing endoscopic biopsy were examined by 4 pathologists by pictorial templates. Changes for 9 microscopic features were recorded as normal, mild, moderate or severe, and 2- and 4-point scales were tested for consistency of interpretation. Logistic regression models determined odds ratios (OR) of histologic lesions being associated with hypoalbuminemia while kappa statistics determined agreement between pathologists on histologic lesions. RESULTS: There was poor agreement (kappa = -0.013 to 0.3) between pathologists, and institution of origin of slides had effect (kappa = 1.0 for 3 of 4 lesions on slides from Institution 5) on agreement between pathologists on selected histologic features. Using 2 point as opposed to 4-point grading scale increased agreement between pathologists (maximum kappa = 0.69 using 4-point scale versus maximum kappa = 1.0 using 2-point scale). Significant association (P = .019- .04; 95% OR = 3.14-10.84) between lacteal dilation and hypoalbuminemia was found by 3 pathologists. CONCLUSIONS AND CLINICAL IMPORTANCE: Substantial inconsistency between pathologists remains despite use of pictorial template because of differences in slide processing. Distinguishing between mild and moderate lesions might be important source of the disagreement among pathologists.
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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 | 0.014 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".