Latent profiles of post-traumatic stress disorder symptoms and their association with recovery trajectories in active transportation injury survivors
Bibliographic record
Abstract
INTRODUCTION: This study aimed to identify distinct post-traumatic stress disorder (PTSD) symptom patterns following active transport injury, determine predictors of pattern membership, and estimate trajectories of 12-month recovery outcomes within each identified PTSD pattern. METHODS: This prospective inception cohort study analyzed data of 1636 survivors recruited in British Columbia, Canada, between January 2022 and December 2023. PTSD symptoms were assessed using the Post-Traumatic Stress Disorder Checklist-Specific (PCL-S) at 2 months post-injury. The recovery outcome of health-related quality of life (HRQoL) was measured at baseline (prior to injury) and at 2, 4, 6, and 12 months post-injury. Self-reported recovery outcomes, including full recovery, return to work or study, daily activities, and recreational activities, were also evaluated at 2, 4, 6, and 12 months post-injury. RESULTS: Three distinct PTSD symptom patterns were identified: Low symptoms (n = 1047, 64.0 %), Moderate symptoms (n = 400, 24.4 %), and High symptoms (n = 189, 11.6 %). Patients in High symptoms group were more likely to be younger, female, pedestrians, having higher pre-injury pain catastrophizing, greater psychological distress, lower pre-injury HRQoL, no expectation of a fast recovery, pre-existing bodily complaints, higher injury pain scores, and injuries to the head, face, neck, or torso. Recovery trajectories revealed that the High symptom group experienced sharp and persistent declines in HRQoL and consistently lower probabilities of full recovery, return to work or study, and engagement in daily or recreational activities compared to other groups. CONCLUSIONS: This study highlights the heterogeneity of PTSD symptom patterns following injury and underscores the need to address PTSD symptoms as an important part of the recovery process.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".