Patterns of Injury in Children: A Population-Based Approach
Notice bibliographique
Résumé
OBJECTIVE: We describe the frequency and patterns of injury affecting 96 359 children between 0 and 10 years old and living in Alberta, Canada. DESIGN: This population-based, longitudinal study involved children born in the 3 fiscal years of April 1, 1985 to March 31, 1988, recruited before age 1, and who remained in the study until at least age 5. We used the International Classification of Diseases, Ninth Revision, Clinical Modification chapter-17 diagnostic codes provided by physicians. Codes were grouped into 17 categories; injury episodes were calculated, and age- and gender-specific incidence rates for each category were calculated. The age, pattern, times of greatest risk, and the effect of gender on the type and incidence of injury were determined. SETTING: Health care administrative data were obtained from all fee-for-service health care venues in Alberta between April 1, 1985 and March 31, 1998 providing services to children registered with the Alberta Health Care Insurance Plan and otherwise meeting entrance criteria. RESULTS: Nearly 84% of children received care for an injury during the study period, and in any given year approximately 21% of the population studied had at least 1 injury. Repeat injury was common (73%), and boys were more likely than girls to be injured and to have repeat injury. The most common injuries were dislocations and sprains, open wounds, and superficial injuries and contusions. Burns, poisoning, intracranial injury, and foreign bodies were the next most common, and fractures were least common. Approximately 10% of injuries were multiple-category injuries. Rates varied greatly by injury category, age, and gender. Hospitalization rates varied in a similar manner and commonly accounted for approximately 10% of all services. Males were most likely to have an injury, and aboriginal children or children who had received welfare at some time were at greatest risk. CONCLUSIONS: Administrative data can be used to estimate the incidence of injury in a pediatric population. Distinct patterns of injury occur at different ages. Recurrent injury is common. Almost identical proportions of injury (46%) are treated in emergency departments and physicians' offices.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,000 | 0,000 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».