The role of pain coping strategies in prognosis after whiplash injury: Passive coping predicts slowed recovery
Bibliographic record
Abstract
Pain coping strategies are associated with pain severity, psychological distress and physical functioning in populations with persistent pain. However, there is little evidence regarding the relationship between coping styles and recovery from recent musculoskeletal injuries. We performed a large, population-based prospective cohort study of traffic injuries to assess the relationship between pain coping strategies and recovery from whiplash injuries. Subjects were initially assessed within 6 weeks of the injury, with structured telephone interview follow-up at 6 weeks, and 3, 6, 9 and 12 months post-injury. Coping was measured at 6 weeks using the Pain Management Inventory and recovery was assessed at each subsequent follow-up period, using a global self-report question. Multivariable Cox proportional hazards models showed that early use of passive coping strategies was independently associated with slower recovery. Depressive symptomatology (CES-D) was an effect modifier of this relationship. Without depressive symptomatology, those using high levels of passive coping recovered 37% slower than those using low levels of passive coping (HRR=0.63; 95% CI 0.44-0.91). However, in the presence of depressive symptomatology, those using high levels of passive coping recovered 75% more slowly than those who coped less passively (HRR=0.25; 95% CI 0.17-0.39). In other words, those with depressive symptoms but who used few passive coping strategies recovered four times more quickly than those with depressive symptoms who used high levels of passive coping. Active coping showed no independent association with recovery. These findings highlight the importance of early assessment of both coping behaviors and depressive symptomatology.
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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 | 0.004 | 0.001 |
| 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) | 0.000 | 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".