Stress and pain systems : a meta-analysis of their complex physiological interplay
Bibliographic record
Abstract
Background and aims: Besides psychological distress, (dys)functioning of stress systems, i.e. the autonomic nervous system (ANS) and hypothalamic-pituitary-adrenal (HPA-)axis, has been implicated in pain. However, the exact interplay between (re)activity of stress and pain systems in chronic pain remains unclear. This study will synthesize the evidence regarding their interactions in chronic pain. Methods: A systematic review and meta-analysis was pre-registered on PROSPERO (CRD42024495934). Six databases were searched to identify studies examining at least one physiological stress marker of ANS or HPA-axis reflecting basal levels (i.e., before a stressor), reactivity (i.e., during a stressor) and/or recovery (i.e., after a stressor), and one experimental outcome measure of pain in adults with chronic primary pain. Risk of bias (RoB) was evaluated with the Newcastle-Ottawa Scale, and certainty of evidence (CoE) with GRADE. Results: Forty-six studies (3 cross-sectional, 43 case-control; n=2407) were included and scored averagely 9/12 (5-11) on RoB. Overall CoE was (very) low. At baseline, qualitative analyses showed significant correlations between lower mean arterial pressure and higher pain sensitivity in patients with chronic pain which was confirmed in the meta-analyses (r=.301-.353, p=.02-.033). Furthermore, meta-analyses showed that higher cortisol levels were associated with lower pressure pain thresholds (PPTs) at baseline (r=.218, p=.01). Higher heart rate was associated with lower PPTs, and lower highfrequency heart rate variability measured at recovery level with lower cold pain tolerance (r=.309-.525, p=.001-.028). Conclusions: Dysregulation of baroreceptor and HPA-axis functioning seems to be related to higher pain sensitivity at baseline, and autonomic dysfunction might be related to higher pain sensitivity under acute stress in patients. However, the evidence is low and limited.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.047 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".