Battered pets‘: non‐accidental physical injuries found in dogs and cats
Bibliographic record
Abstract
Records of 243 cases of non-accidental injury (NAI) in dogs, and 182 cases in cats, submitted by a sample of small animal practitioners in the UK, revealed a wide range of injuries. These included bruises, fractures, repetitive injuries, burns and scalds, stab and incised wounds, poisoning, asphyxiation and drowning (which showed remarkable similarities to NAI in children), as well as sexual abuse and injuries specifically caused by firearms. Traumatic skeletal injuries in the dogs were more commonly found in the anterior part of the skeleton, in comparison with those resulting from road traffic accidents. Young male dogs and young cats were particularly at risk of NAI. A moderately increased risk was identified in the Staffordshire bull terrier, cross-breed dogs and the domestic shorthaired cat, whereas the Labrador retriever showed a decreased risk. No single injury or group of injuries, when divorced from the circumstances surrounding a suspect case, could be considered to indicate, conclusively, NAI. Repetitive injuries, however, were highly suggestive of NAI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".