Longitudinal modeling of health-related quality of life trajectories over 12 months following road trauma
Bibliographic record
Abstract
INTRODUCTION: This study identified the potential phases of health-related quality of life (HRQoL) change over a year following road trauma (RT) injury, as well as its predictors. METHODS: This inception cohort study recruited 1480 Canadian RT survivors from July 2018 to March 2020. HRQoL outcome was assessed with the 5-level version of the EuroQol (EQ-5D-5L) instrument at baseline (pre-injury) and at 2, 4, 6, and 12 months post-injury. Predictors of HRQoL included sociodemographic, psychological, medical, and trauma-related factors collected at baseline. We applied generalized additive mixed models to flexibly capture nonlinear changes in HRQoL over time, and piecewise latent growth curve model to analyze distinct linear phases of recovery across defined time intervals. RESULTS: The estimated trajectory of EQ-5D-5L summary and EQ-VAS scores were lower than baseline at 2-months (phase 1), and then increased (phase 2), but did not return to baseline values at 12-months. White ethnicity, higher somatic symptom, pain catastrophizing, and use of medication pre-injury were associated with lower pre-injury EQ-5D-5L summary and EQ-VAS scores. Phase 1 EQ-5D-5L decreases were associated with female sex, no pre-existing body complaints, lack of expectation for a fast recovery, higher ISS, higher injury pain, and neck, spine/back, upper extremity, or lower extremity injuries. Phase 1 EQ-VAS decreases were associated with female sex, lower somatic symptom, fewer comorbidities, lack of expectation for a fast recovery, higher ISS, higher injury pain, neck, spine/back or lower extremity injuries. In phase 2, EQ-5D-5L summary improved most in participants with higher education levels and longer recovery expectations; EQ-VAS improved most in cyclists and patients with longer recovery expectations. CONCLUSIONS: Clinicians should assess and address patients' recovery expectations early in the care process, as these may significantly influence long-term HRQoL outcomes. Incorporating strategies to support realistic yet positive expectations, such as cognitive-behavioral therapy, structured patient education, and goal-setting programs, may improve recovery experiences. In addition, identifying patients with high pain, or specific injury types may help target early interventions to those at risk of poor HRQoL trajectories.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".