Trajectories of health-related quality of life following road trauma: Latent growth mixture modeling across a 12-month cohort study
Notice bibliographique
Résumé
INTRODUCTION: Health-related quality of life (HRQoL) should be considered when evaluating the burden of road trauma (RT) injuries. This study aimed to identify distinct HRQoL trajectories following minor to severe RT injury and determine characteristics of trajectory membership. METHODS: This prospective inception cohort study recruited 1480 RT survivors from three emergency departments in British Columbia, Canada (July 2018 - March 2020). HRQoL outcome was measured with the Short Form 12 survey (SF-12) and the 5-level version of the EuroQol instrument (EQ-5D-5L) at baseline (pre-injury) and at 2, 4, 6, and 12 months post-injury. Potential predictors of outcome trajectory included sociodemographic, psychological, medical, crash, and injury factors collected at baseline. We used a latent growth mixture model to identify distinct recovery trajectories and multinomial logistic regression to determine predictors of trajectory membership. RESULTS: Three distinct HRQoL trajectories were identified for SF-12 subscales and EQ-5D-5L measures: Low/Moderate-Stable, High-Large decline, and High-Slight decline. Participants in the Low/Moderate-Stable trajectory had persistent low to moderate HRQoL before and after the injury. Those in the High-Large decline trajectory had good pre-injury HRQoL followed by persistently decreased HRQoL afterwards. The High-Slight decline trajectory was characterized by good pre-injury HRQoL and only a slight decline afterwards. Participants in the Low/Moderate-Stable and High-Large decline trajectories were considered at risk of permanently poor HRQoL following RT injury given their low HRQoL over a long period of time. Characteristics that placed participants in the Low/Moderate-Stable trajectory were older age, female gender, poor pre-injury health (medical comorbidity, prescribed medication use, complaints in the injured body area(s)), pre-injury somatic symptoms, pain catastrophizing or psychological distress, injury severity (ISS) and injury pain. Patients with head injury were less likely to be in the Low/Moderate-Stable trajectory. Risk factors for membership in the High-Large decline trajectory included older age (for physical HRQoL), younger age (for mental HRQoL), female gender, living alone, pre-injury psychological distress, ISS, injury pain, no expectations for a fast recovery, as well as head injuries, spine/back injuries or lower extremity injuries. CONCLUSIONS: This study highlighted the heterogeneity of HRQoL trajectories following RT injury and the importance of considering differences between characteristics of survivors. In addition to injury type and severity, outcome is related to demographic factors, pre-injury health and pre-injury psychological factors.
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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,002 |
| 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 ».