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 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 | 0.002 |
| 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".