Coping and Recovery in Whiplash-associated Disorders
Bibliographic record
Abstract
OBJECTIVE: Coping is shown to affect outcomes in chronic pain patients; however, few studies have examined the role of coping in the course of recovery in whiplash-associated disorders (WAD). The purpose of this study was to determine the predictive value of coping style for 2 key aspects of WAD recovery, reductions in neck pain, and in disability. METHODS: A population-based prospective cohort study design was used to study 2986 adults with traffic-related WAD. Participants were assessed at baseline, 6 weeks, and 4, 8, and 12 months postinjury. Coping was measured at 6 weeks using the Pain Management Inventory, and neck pain recovery was assessed at each subsequent follow-up, using a 100 mm visual analogue scale (VAS). Disability was assessed at each follow-up using the Pain Disability Index (PDI). Pain recovery was defined as a VAS score of 0 to 10; disability recovery was defined as a PDI score of 0 to 4. Data analysis used multivariable Cox proportional hazards models. RESULTS: Those using high versus low levels of passive coping at 6 weeks postinjury experienced 28% slower pain recovery and 43% slower disability recovery. Adjusted hazard rate ratios for pain recovery and disability recovery were 0.72 (95% CI, 0.59-0.88) and 0.57 (95% CI, 0.41-0.78), respectively. Active coping was not associated with recovery of neck pain or disability. CONCLUSIONS: Passive coping style predicts neck pain and self-assessed disability recovery. It may be beneficial to assess and improve coping style early in WAD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".