Paediatric dog bite injuries: a 10‐year retrospective cohort analysis from Sydney Children's Hospital
Bibliographic record
Abstract
BACKGROUND: Dog bite injuries are largely preventable yet present the most common animal related cause of hospitalisation. This study examines the demographics and clinical cost of patients with dog bite related injuries who presented to Sydney Children's Hospital (SCH) from 2010 to 2020. The results from our study will be used to raise awareness regarding the impact of dog bite injuries in our community. METHODS: Data was obtained from the SCH database using ICD-10-AM code W54.0, which captures all patients presenting to SCH with dog bite injuries from 2010 to 2020. A chart review was then performed to retrieve demographic data for analysis. Data analysis was performed using SAS® software version 9.4 and cost for each patient retrieved from the SCH clinical costing department. RESULTS: A total of 628 patients presented to SCH with dog bites during the study period. 273 (43.5%) patients received treatment in ED only with the remaining 355 (56.5%) patients admitted for treatment. The average age was 5.69 years old. There were 321 males (51.1%) and 307 females (48.9%). Facial and other head & neck injuries were most common (64.4%). Pitbull, Labrador and Rottweiler were the most commonly documented offending breeds (25%) with the family dog most likely to offend (49%). The mean clinical cost for per dog bite injury was $2968. CONCLUSION: As part of the largest single centre study exploring dog bite injuries, we expect that this study will stimulate potential public health campaigns targeted at educating parents and children on interacting with dogs to minimise these injuries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".