Household hostilities: A descriptive study of inter-dog aggression requiring veterinary treatment of dog bite wounds in Pretoria, South Africa
Bibliographic record
Abstract
Inter-dog aggression (IDA) places a high burden on the dogs involved, their owners and their households. Treating dog bite wounds (DBW) accounts for a substantial proportion of small animal veterinary practice caseload. This study aimed to identify potential risk factors of IDA in dogs presented for the treatment of DBW at a veterinary teaching hospital in Pretoria, South Africa. Veterinary staff completed a survey regarding wound severity, distribution, treatment, and outcome of 126 dogs treated for DBW. A separate, but related survey was completed by 124 owners of dogs presenting for DBW, describing the fighting event, dogs involved, and the household context where these fighting dogs lived. Control household data was collected from surveys completed by 71 owners of dogs being treated for alternative conditions, where no household dogs had been treated for DBW by a veterinarian. Most fighting between dogs occurred on the owner’s property (85.4%) and between household dogs (68.5%). From the 83 household pairs where the sex and sterilisation status were known, fighting was more common between dogs of the same sex (71%) and sterilisation status (53%). Fighting pairs were most frequently both intact male (25%) or both sterilised female dogs (16%). Compared to control households, dog bite households kept on average significantly more dogs (4.14 compared to 3.44 dogs, p = 0.029) and significantly more male intact dogs (1.04 compared to 0.66 dogs, (p = 0.043). Breeds over-represented in dog bite households were Boerboels (p = 0.043), German Shepherd dogs (p = 0.034) and Pitbull Terriers (p = 0.002) compared to control household. Breeds under-represented in dog bite households were Dachshunds (p = 0.046), Labrador Retrievers (p = 0.026), Miniature Poodles (p = 0.016) and Schnauzers (p = 0.032) compared to control households. Few biting incidents occurred during supervised walks (4%), which differs substantially from previous studies, which reported that most fights between dogs occured in public spaces involving unleashed dogs. Based on our study findings, the following locally relevant IDA prevention measures are indicated: limiting the number of household dogs to three or fewer, reducing the number of male intact dogs, mixing sexes, and avoiding Boerboels, German Shepherds and Pitbull Terriers breeds in multidog households.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".