Effect of Sample Quality on the Sensitivity of Endoscopic Biopsy for Detecting Gastric and Duodenal Lesions in Dogs and Cats
Bibliographic record
Abstract
BACKGROUND: The quality of histopathology slides of endoscopic biopsies from different laboratories varies, but the effect of biopsy quality on outcome is unknown. HYPOTHESIS: The ability to demonstrate a histologic lesion in the stomach or duodenum of a dog or cat is affected by the quality of endoscopic biopsy samples submitted. More endoscopic samples are needed to find a lesion in poor-quality tissue specimens. ANIMALS: Tissues from 99 dogs and 51 cats were examined as clinical cases at 8 veterinary institutions or practices in 5 countries. METHODS: Histopathology slides from sequential cases that underwent endoscopic biopsy were submitted by participating institutions. Quality of the histologic section of tissue (inadequate, marginal, adequate), type of lesion (lymphangiectasia, crypt lesion, villus blunting, cellular infiltrate), and severity of lesion (normal, mild, moderate, severe) were determined. Sensitivity of different quality tissue samples for finding different lesions was determined. RESULTS: Fewer samples were required from dogs for diagnosis as the quality of the sample improved from inadequate to marginal to adequate. Duodenal lesions in cats displayed the same trend except for moderate duodenal infiltrates for which quality of tissue sample made no difference. Gastric lesions in dogs and mild gastric lesions in cats had the same trend, whereas the number of tissue samples needed to diagnose moderately severe gastric lesions in cats was not affected by the quality of tissue sample. CONCLUSIONS AND CLINICAL IMPORTANCE: The quality of endoscopically obtained tissue samples has a profound effect on their sensitivity for identifying certain lesions, and there are differences between biopsies of canine and feline tissues.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".