Secondary analysis: heat and self-report pain sensitivity associate with biological sex and racialized sociocultural group but may not be mediated by anxiety or pain catastrophizing
Bibliographic record
Abstract
Abstract Introduction: Previous studies have demonstrated associations between sex and racialized group on pain sensitivity and tolerance. We analyzed the association of sex and racialized group on heat pain sensitivity, sensibility to painful suprathreshold mechanical pain (STMP), and pain sensitivity questionnaire (PSQ). We hypothesized that anxiety and pain catastrophizing reported by racialized minority groups and women would mediate enhanced pain sensitivity. Our secondary aim was to evaluate validity of the PSQ in a diverse population. Methods: Using quantitative sensory testing for painful heat, STMP (forces: 64, 128, 256, and 512 mN), and PSQ, we evaluated pain sensitivity in 134 healthy participants [34 (18 women) Asian, 25 (13 women) Black, and 75 (41 women) White]. We used general linear and linear mixed models to analyze outcomes. We assessed mediation of state and trait anxiety and pain catastrophizing on pain sensitivity. Results: Racialized minority status was associated with greater heat pain sensitivity (F = 7.63; P = 0.00074) and PSQ scores (F = 15.45; P = 9.84 × 10−7) but not associated with STMP (F = 1.50; P = 0.23). Female sex was associated with greater heat pain sensitivity (F = 4.9; P = 0.029) and lower PSQ (F = 9.50; P = 0.0025) but not associated with STMP (F = 0.0018; P = 0.97). Neither anxiety nor pain catastrophizing mediated associations between sex or racialized group with heat pain threshold or PSQ. Differential experience of individual items (F = 19.87; P = 3.28 × 10−8) limited PSQ face validity in racialized minorities. Conclusion: Consistent with previous research, sensitivity to painful heat was associated with racialized minority status and female sex. By contrast, there was no significant effect of racialized minority status or female sex on STMP. Some PSQ items are inapplicable to participants from racialized minority groups.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".