Dog Bites in Children: A Descriptive Analysis
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle