A predictive model to analyze factors affecting the presence of mild whiplash-associated disorders in minor motor vehicle crashes based on the Korean In-Depth Accidents Study (KIDAS) Database
Notice bibliographique
Résumé
OBJECTIVES: We aimed to analyze factors affecting the severity of mild whiplash-associated disorders (WADs) and to develop a predictive model to evaluate the presence of mild WAD in minor motor vehicle crashes (MVCs). METHODS: We used the Korean In-Depth Accident Study (KIDAS) database, which collects data from 4 regional emergency centers, to obtain data from 2011 to 2017. The Collision Deformation Classification code was obtained as vehicle's damage information, and Abbreviated Injury Scale (AIS), Maximum Abbreviated Injury Scale (MAIS), and Injury Severity Score (ISS) were used as occupant's injury information. The degree of WAD was determined using the Quebec Task Force (QTF) classification, comprised of 5 stages (QTF 0-4), depending on the occupant's pain and the physician's findings. QTF 1 was defined as mild WAD, and we used QTF 0 to define those who were uninjured. For KIDAS data between 2011 and 2016, a logistic regression model was used to identify factors affecting the occurrence of mild WAD and a predictive model was constructed. Internal validity was estimated using random bootstrapping, and external validity was evaluated by applying 2017 KIDAS data. Of the 2,629 occupants in the KIDAS database from 2011 to 2016, after applying several exclusion conditions, 459 occupants were used to develop the predictive model. The external validity of the derived predictive model was assessed using the 13 MVC occupants from the 2017 KIDAS database meeting our inclusion criteria. Among the 137 MVC occupants from the 2017 KIDAS database for analysis of the external validity of the derived predictive model, the predictive model was verified for 13 MVC occupants. RESULTS: Logistic regression analysis was used to derive a predictive model based on sex, age, body mass index, type of vehicle, belt status, seating row, crush type, and crush extent. This predictive model had an explanatory power of 65.5% to determine an actual QTF of 0 and 1 (c-statistics: 0.655). As a result of the external validity analysis of the predictive model using data from the 2017 KIDAS database (N = 13), sensitivity, specificity, and accuracy were 0.500, 0.857, and 0.692, respectively. CONCLUSIONS: Using the predictive model, the results of the external validity analysis showed low sensitivity but high specificity. This predictive model provided meaningful results, with a high success rate for determining no injury to an occupant. Given our study results, future research is needed to create a more accurate predictive model that includes relevant technical and sociological factors.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 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 ».