Dog Bites in Children: A Descriptive Analysis
Bibliographic record
Abstract
Objective: To describe characteristics of dog bites and their treatment in a pediatric population including infection, medical specialties involved, rates of admission, and need for surgery. Method: Patients presenting with a dog bite to the emergency department of a tertiary care pediatric hospital between January 1, 2015, and June 30, 2017, were included. Details related to demographics, complications, consultations, and treatment were extracted from the patients’ records. Descriptive statistics were performed and binary logistic regression was used to assess potential predictors of infection. Results: One hundred fifty-eight dog bite patients were identified. Most patients were male (53.8%) and less than 5 years of age (50%). Bites occurred most frequently in June (13.3%) and July (16.5%). The face was most commonly involved (42.9%), followed by the hands (12.6%) and the scalp (26.6%). Pit bulls (11.4%), Labrador retrievers (7.0%), and German shepherds (4.4%) were the most common offending breeds. Most bites were superficial (91.1%). Half were treated conservatively with dressings and petrolatum-based ointment, with 41.1% requiring simple primary closure. Ten (6.3%) cases necessitated primary repair in the main operating room under general anesthesia. More than half of patients were treated with prophylactic systemic antibiotics (55.1%). Plastic surgery was the most common service involved (24.7%). Seven (4.4%) patients developed an infection and there were no mortalities or long-term complications. Rates of infection did not differ between patients who did or did not receive prophylactic systemic antibiotics ( P = .88). Regression analysis revealed no significant predictors of infection. Conclusions: Most dog bites are superficial and involve the head and hands. Infection rate is low, with no significant difference in infection rates between patients treated with or without prophylactic antibiotics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".