Health-related quality of life in the year following road trauma: Longitudinal analysis using piecewise latent curve modeling
Bibliographic record
Abstract
BACKGROUND: Road trauma (RT) survivors have reduced health-related quality of life (HRQoL). We identified phases and predictors of HRQoL change following RT injury. METHODS: In a prospective cohort study of 1480 Canadian RT survivors aged 16 to 103 years (July 2018 - March 2020), physical component (PCS) and mental component (MCS) summary scores from the SF-12v2 were measured pre-injury and 2, 4, 6, and 12 months post-injury and their trajectories were analyzed with piecewise latent growth curve modeling. Potential predictors of HRQoL changes included sociodemographic, psychological, medical, and trauma-related factors. RESULTS: PCS and MCS scores worsened from pre-injury to 2-months (phase 1) and then improved (phase 2), but never regained baseline values. Older age, somatic symptoms and pain catastrophizing were associated with lower preinjury PCS and MCS scores. Psychological distress was associated with lower preinjury MCS scores and higher preinjury PCS scores. Phase 1 PCS scores decreased most in females, participants with fewer pre-injury somatic symptoms and those without expectations for fast recovery. Phase 1 MCS decreases were associated with younger age, female sex, living alone, lower psychological distress, lack of expectation for fast recovery and higher injury pain. In phase 2, MCS improved most in participants not using recreational drugs; PCS improved most in participants with higher education and longer recovery expectations. LIMITATIONS: There may be recall bias with reporting pre-injury HRQoL. Selection bias is possible. CONCLUSIONS: Many factors influence HRQoL following RT. These findings may inform measures to minimize HRQoL reduction following RT and speed up subsequent recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".