Dog Bites in the United States from 1971 to 2018: A Systematic Review of the Peer-Reviewed Literature
Bibliographic record
Abstract
BACKGROUND: Surgical specialists in plastic, head and neck, hand, trauma surgery, and emergency medicine physicians bear the burden of treating the most serious injuries caused by animals. Most of these incidents result from an attack by a known dog, and breed has been proposed, but not proven, to be a controllable factor. The authors summarize the peer-reviewed literature on dog bites in the United States, specifically as related to the breeds implicated. METHODS: A systematic review of all peer-reviewed publications reporting on dog bites in the United States was performed. MEDLINE, Embase, Scopus, Google Scholar, and Cochrane Library searches were conducted through May 8, 2018, for studies from the United States implicating a specific dog breed as responsible. RESULTS: Forty-one articles met inclusion criteria, the majority of which were single-institution retrospective reviews. Main outcomes were any dog bite reported in the peer-reviewed literature where a specific breed was implicated. Secondary measures included dog bites reported in areas where breed-specific legislation was enacted. The most common pure breed identified was German Shepherd, followed by Pit Bull-type breeds (i.e., American Staffordshire Terrier, American Pit Bull Terrier, Staffordshire Bull Terrier, American Bully), Labrador, Collie, and Rottweiler, respectively. Pit bull-type and German Shepherd breeds are consistently implicated for causing the most serious injuries to patients in the United States across heterogeneous populations, and this remained consistent across multiple decades. CONCLUSIONS: The authors' results indicate that German Shepherd and Pit Bull-type breeds account for the largest subset of pure breeds implicated in severe dog bites inflicted on humans in the medical literature. The role and complexity of mentioning breed in relation to human injuries are also discussed.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".