Negative effect of anger on chronic pain intensity is modified by multiple mood states other than anger: A large population-based cross-sectional study in Japan
Bibliographic record
Abstract
OBJECTIVES: To investigate whether mood states other than anger can modify the association between anger and pain intensity in individuals with chronic pain. METHODS: We analysed 22,059 participants with chronic pain, including 214 participants with rheumatoid arthritis (RA), who completed a questionnaire. The Profile of Mood States short form (POMS-SF) was used to assess six dimensions of mood states (anger-hostility, tension-anxiety, depression-dejection, confusion, fatigue, and vigour). A numerical rating scale (NRS) assessed pain intensity. We examined the association between anger-hostility and the NRS and the relationship between POMS-SF components. Moderation analyses were used to investigate whether the five mood states other than anger-hostility modified the effect of anger-hostility on the NRS. RESULTS: Anger-hostility contributed to pain intensity. Although increased mood states other than vigour were associated with increased pain intensity, these increased mood states appeared to suppress the effect of anger-hostility on pain intensity. Increased vigour was associated with decreased pain intensity and increased the effect of anger-hostility on pain intensity. CONCLUSIONS: Mood states other than anger may influence the association between anger and pain intensity in individuals with chronic pain. It is important to focus on complicated mood states and anger in individuals with chronic pain, including RA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".