Differential effects of expressive anger regulation on chronic pain intensity in CRPS and non-CRPS limb pain patients
Bibliographic record
Abstract
Research has shown that the anger management styles of both anger-in (suppression of anger) and anger-out (direct verbal or physical expression of anger) may be associated with elevated chronic pain intensity. Only the effects of anger-out appear to be mediated by increased physiological stress responsiveness. Given the catecholamine-sensitive nature of pain mechanisms in complex regional pain syndrome (CRPS), it was hypothesized that anger-out, but not anger-in, would demonstrate a stronger relationship with chronic pain intensity in CRPS patients than in non-CRPS chronic pain patients. Thirty-four chronic pain patients meeting IASP criteria for CRPS and 50 non-CRPS (predominantely myofascial) limb pain patients completed the McGill Pain Questionnaire-Short Form (MPQ), the Anger Expression Inventory (AEI), and the Beck Depression Inventory (BDI). Analyses revealed no diagnostic group differences in mean scores on the anger-in (AIS) and anger-out (AOS) subscales of the AEI, or on the BDI (values of P>0.10). Results of general linear model analyses revealed significant AOS x diagnostic group interactions on both the sensory (MPQ-S) and affective (MPQ-A) subscales of the MPQ (values of P<0.05). In both cases, higher AOS scores were associated with more intense chronic pain in the CRPS group, but with less intense pain in the non-CRPS limb pain group. Inclusion of BDI scores as a covariate did not substantially alter the AOS x diagnostic group interactions, indicating that these AOS interactions were not due solely to overlap with negative affect. Although higher AIS scores were associated with elevated MPQ-A pain intensity as a main effect (P<0.05), no significant AIS x diagnostic group interactions were detected (values of P>0.10). The AIS main effect on MPQ-A ratings was accounted for entirely by overlap with negative affect. Results are consistent with a greater negative impact of anger-out on chronic pain intensity in conditions reflecting catecholamine-sensitive pain mechanisms, presumably due to the association between anger-out and elevated physiological stress responsiveness. These results further support previous suggestions that anger-in and anger-out may affect pain through different mechanisms.
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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 | 0.001 | 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) | 0.002 | 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".