Trajectories of health-related quality of life following road trauma: Latent growth mixture modeling across a 12-month cohort study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".