Effects of acute stress exposure on pain sensitivity: the role of individual stress responsiveness and orientation to pain and stress
Bibliographic record
Abstract
ABSTRACT: Acute stress exposure has been reported to result in stress-induced hypoalgesia (SI-hypo), hyperalgesia (SI-hyper), or no response (SI-NR). The inconsistency may stem from individual variability in acute stress response and/or individual orientation to stress and pain, factors not commonly examined. We aimed to identify patterns of SI-hypo and SI-hyper, their relation to stress responsiveness, and the moderating effects of stress and pain orientations. Healthy participants (n = 133) were exposed to acute stress via the Montreal Imaging Stress Task (MIST). Heat-pain threshold (HPT) was measured before and after the MIST. Changes in state anxiety pre-post MIST indicated stress responsiveness, and stress and pain orientations were evaluated via Global Perceived Stress (GPS) and Fear of Pain (FOP), respectively. Autonomic variables and salivary cortisol were examined for manipulation check. The MIST induced three pattens of effect on HPT: 27.8% of participants exhibited SI-hypo, 24.8% exhibited SI-hyper, and 47.4% SI-NR. Higher stress responsiveness was associated with greater HPT change. FOP and GPS significantly moderated the association between stress responsiveness and HPT change; the higher the stress response, the greater SI-hypo among individuals with low FOP and among individuals with high GPS. Furthermore, the higher the stress response, the greater SI-hyper among individuals with high FOP. Thus, individual variability in the effects of stress on pain sensitivity may be conditioned by stress responsiveness, and by stress and pain orientations. As increased distress can contribute to, and exacerbate, chronic pain, FOP and GPS may serve as treatment targets for the prevention and amelioration of chronic pain.
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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".