Catastrophizing as a mediator of sex differences in pain: differential effects for daily pain versus laboratory-induced pain
Bibliographic record
Abstract
Sex differences in the experience of pain have been widely reported, with females generally reporting more frequent clinical pain and demonstrating greater pain sensitivity. However, the mechanisms underpinning such differences, while subject to intense speculation, are not well-characterized. Catastrophizing is a cognitive and affective process that relates strongly to enhanced reports of pain and that varies as a function of sex. It is thus a prime candidate to explain sex differences; indeed, several prior studies offer evidence that controlling for catastrophizing eliminates the gap between men and women in reported pain. We recruited 198 healthy young adults (115 female) who took part in laboratory studies of pain responses, including thermal pain, cold pain, and ischemic pain, and who also completed questionnaires assessing catastrophizing, mood, and day-to-day painful symptoms (e.g. headache, backache). Women reported greater levels of catastrophizing, more recent painful symptoms, and demonstrated lower pain thresholds and tolerances for noxious heat and cold relative to men. Mediational analyses suggested that after controlling for negative mood, catastrophizing mediated the sex difference in recent daily pain but did not mediate the much larger sex differences in pain threshold and tolerance. These findings highlight the role of catastrophizing in shaping pain responses, as well as illuminating potentially important differences between experimental pain assessment and the clinical experience of 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".