Feasibility, complications, and quality of visualization using video capsule endoscopy in 40 dogs with overt or questionable gastrointestinal bleeding
Bibliographic record
Abstract
BACKGROUND: Prospective studies describing video capsule endoscopy (VCE), its feasibility, and complications in dogs are limited. OBJECTIVE: To assess VCE, quality of visualization, complications, and risk factors for incomplete studies in dogs with overt or questionable gastrointestinal bleeding (GIB). ANIMALS: Forty dogs with overt or questionable GIB. METHODS: Prospective, multicenter, interventional study. From August 2017 to March 2020, dogs were examined by VCE (ALICAM) because of overt or questionable GIB. Reported outcomes included diagnostic results of VCE study, quality of visualization, and complications. Risk factors for incomplete studies were evaluated using logistic regression. RESULTS: In total, 40 dogs (13 overt, 27 questionable GIB) were included. The capsules were administered PO in 29 and endoscopically in 11 dogs (6 duodenum, 5 stomach). One capsule was not retrieved. In 24 of 39 recordings, bleeding lesions were identified (10 overt GIB, 14 questionable GIB). Overall, the quality of visualization was poor to limited in the stomach and colon, and adequate to good in the small intestine. The most common complication was an incomplete study in 15/39 studies, particularly after oral administration (13/28). Risk factors for incomplete study after oral administration included administration of simethicone or opioids, chronic enteropathy, and capsule gastric transit time >6 hours. CONCLUSIONS AND CLINICAL IMPORTANCE: Video capsule endoscopy can be used to diagnose a variety of lesions causing bleeding in the gastrointestinal tract of dogs with questionable GIB. Incomplete studies are the most common complications in dogs after oral administration of capsules.
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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 | 0.004 | 0.008 |
| 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 | 0.001 | 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".