Incidence of and breed-related risk factors for gastric dilatation-volvulus in dogs
Bibliographic record
Abstract
OBJECTIVE: To compare incidence of and breed-related risk factors for gastric dilatation-volvulus (GDV) among 11 dog breeds (Akita, Bloodhound, Collie, Great Dane, Irish Setter, Irish Wolfhound, Newfoundland, Rottweiler, Saint Bernard, Standard Poodle, and Weimaraner). DESIGN: Prospective cohort study. ANIMALS: 1,914 dogs. PROCEDURE: Owners of dogs that did not have a history of GDV were recruited at dog shows, and the dog's length and height and depth and width of the thorax and abdomen were measured. Information concerning the dogs' medical history, genetic background, personality, and diet was obtained from owners, and owners were contacted by mail and telephone at approximately 1-year intervals to determine whether dogs had developed GDV or died. Incidence of GDV based on the number of dog-years at risk was calculated for each breed, and breed-related risk factors were identified. RESULTS AND CLINICAL RELEVANCE: Incidence of GDV for the 7 large (23 to 45 kg [50 to 99 lb]) and 4 giant (> 45 kg [> 99 lb]) breeds was 23 and 26 cases/1,000 dog-years at risk, respectively. Of the 105 dogs that developed GDV, 30 (28.6%) died. Incidence of GDV increased with increasing age. Cumulative incidence of GDV was 5.7% for all breeds. The only breed-specific characteristic significantly associated with a decreased incidence of GDV was an owner-perceived personality trait of happiness.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".