Management of mammalian bites
Bibliographic record
Abstract
### What you need to know Domestic and wild mammalian bite wounds are increasing.1234 Annually, 15 in 100 000 adults in England are hospitalised for dog bites—rising twofold from 1998 to 2018, faster than the rise in dog numbers.5 A study estimates 17 in 1000 individuals experience animal bites annually, predominantly from dogs, cats, and monkeys.3 Cat bites are highest among women and children and comprise 3% to 25% of all bite wounds, with substantial geographical variation.67 In some areas, dog bites among children increased during the covid-19 pandemic.8 Mammalian bite wounds have high risks of infection and complication910 that can be reduced with proper management. Here we offer generalist and acute care physicians a practical approach to wound management, considerations regarding infection, and public health implications, with a special focus on dogs, cats, and humans. We recommend adapting our guidance to local expertise, resources, and protocols. Seek to identify the mechanism of injury, gather information specific to the animal, and assess risk of infection. Key factors to note are: ### Animal and injury factors ### Patient factors
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".