Pain-Related Emotions in Early Stages of Recovery in Whiplash-Associated Disorders
Bibliographic record
Abstract
OBJECTIVE: Psychological factors such as depression affect recovery after whiplash-associated disorders. This study examined the prevalence of pain-related emotions, such as frustration, anger, and anxiety, and their predictive value for postcrash pain recovery during a 1-year follow-up. METHODS: A population-based prospective cohort study design was used. Self-reported pain-related depression, anxiety, fear, anger, and frustration were assessed using 100-mm visual analog scales (VASs) at 6 weeks after crash in 2986 persons with traffic-related whiplash-associated disorder. Multivariable logistic regression was used to assess the relationship between the intensity of these pain-related emotions and pain recovery at 4 and 12 months after crash. Pain was measured at all time points on a 100-mm VAS, and pain recovery was defined as a score of 10 or lower. RESULTS: Pain-related frustration was the most intense, with a mean score of 52. Only 3% of the cohort reported having no pain-related frustration, and 4% reported no pain-related anxiety. Multivariable logistic regression models revealed that each pain-related emotion increased the risk of failing to recover (odds ratios for each point increase on the 100-mm VAS), ranging from 1.011 to 1.015. Specifically, with each 10-point increase in pain-related emotion, the odds of failing to achieve pain recovery at 4 months was increased by 14% (p < .001) for depression, 15% (p < .001) for anxiety, 11% (p < .001) for fear, 12% (p < .001) for anger, and 11% (p < .001) for frustration. CONCLUSIONS: These findings suggest that it may be beneficial for health care providers to address emotional status related to pain in the first few weeks after a whiplash injury.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".