Dog bite and injury prevention—analysis, critical review, and research agenda
Bibliographic record
Abstract
OBJECTIVES: To analyze Australian dog bite injury data and make international comparisons; to review risk and protective factors relating to the dog, injured person, and environment; and to recommend action for prevention and research. METHODS: Australian dog bite injury data, complemented by detailed Victorian and regional data from routine health records and vital statistics, were analyzed to determine incidence, severity, nature, circumstances, and trends. International comparison data were extracted from published reports. Risk and protective factor studies were selected for review from electronic and bibliographic searches where data were recent, sample sizes substantial, and bias limited. RESULTS: The Australian dog bite death rate (0.004/100,000) is lower than both the United States (0.05-0.07/100,000) and Canadian rates (0.007/100,000). Victorian hospitalized trend rates were stable between 1987 and 1998, but there was a decline for children <5 years (p=0.019) corresponding with a reduction in dog ownership. Children 0-4 years have the highest rate of serious injury, particularly facial. Adults have longer hospitalizations, most frequently for upper extremity injury. Risk factors include: child, males, households with dogs, certain breeds, male dogs, home location, and leashed dog. CONCLUSIONS: Dog bite rates are high and it may therefore be assumed that current preventative interventions are inadequate. Responsible dog ownership, including separating young children from dogs, avoiding high risk dogs, neutering, regulatory enforcement, and standardized monitoring of bite rates are required. Controlled investigations of further risk and protective factors, and validated methods of breed identification, are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".