Traumatic Brain Injury Following Military Deployment: Evaluation of Diagnosis and Cause of Injury
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Notice bibliographique
Résumé
OBJECTIVE: To evaluate the prevalence of delayed traumatic brain injury (TBI) diagnosis and cause of injury that resulted in a TBI diagnosis after military deployment. DESIGN: Medical record notes were reviewed in 2016 from a random sample of 1150 US military service members who had their first-time deployment in 2011 and likely sustained a TBI. Location and cause of the injury were extracted from the progress note for analysis. PARTICIPANTS AND SETTING: Active-duty US military service members who received an International Classification of Diseases, Ninth Revision code for a TBI diagnosis in a military facility. MAIN OUTCOME MEASURES: Presence of TBI, location of injury, cause of injury, and time of diagnosis with respect to deployment. RESULTS: The odds of being diagnosed with a deployment-related TBI were 8 times higher during the first 4 weeks upon return from deployment than the subsequent 32 weeks. The likelihood of diagnosing a deployment-sustained TBI during weeks 5 to 32 was 2 times higher than during 33 to 76 weeks following return from deployment. The proportion of deployment-related TBI diagnoses decreased with time following return from deployment but remained above 40% during weeks 33 to 76. Service branch, gender, race, occupation, and time between TBI diagnosis and return from deployment were significant predictors of deployment-related TBIs. Moving motor vehicle, sports, parachute, and being struck by objects were the top causes of injury in garrison (nondeployed setting), whereas blast produced the majority (66%) of all causes of injuries that resulted in a TBI in the deployed setting. CONCLUSION: The increased incidence rate of a TBI diagnosis following deployment can be attributed to delayed diagnosis of TBI sustained from injuries during deployment. TBIs sustained during deployment can be diagnosed beyond the initial 4 weeks after return from deployment and may continue up to 76 weeks following return from deployment.
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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 | 0,007 | 0,007 |
| 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) | 0,000 | 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 découle