Exploring the association of gender role expectations of pain and measures of pain sensitization in people with knee osteoarthritis: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: First, we explored the association between Gender Role Expectations of Pain (GREP), and psychophysical measures of sensitization in people with knee osteoarthritis (OA). Second, we explored whether the association differed by level of GREP items (high vs low scores). DESIGN: We conducted secondary analyses of a cohort study. Those who were (i) age of ≥40, English or French speaking, ii) diagnosed with knee OA using American College of Rheumatology criteria and iii) consulting with an orthopedic surgeon were included. GREP items pertaining to pain sensitivity and pain endurance of the typical man or woman were rated by males and females respectively. Psychophysical tests consisted of pressure pain thresholds (PPTs), Temporal Summation (TS), and Conditioned Pain Modulation (CPM). Multiple linear regression models for males and females were run with GREP scores (independent variables) and psychophysical tests (dependent variables). Next models stratified on the median split of GREP scores were run. Models were adjusted for age, BMI, pain catastrophizing, anxio-depressive symptoms, and radiographic severity. RESULTS: 280 participants (57% females; age (SD): 63.9 (9.6) and BMI (SD): 31.3 (8.40)) were included. GREP pain sensitivity scores in males were associated with CPM values (β: 95% CI: 0.09 (0.01 to 0.17)). Males with low GREP pain sensitivity or pain endurance had very small to small positive associations with PPT and CPM values. CONCLUSION: This first exploration of gendered pain sensitivity and pain endurance by males and females has small and clinically unimportant associations with measures of pain sensitization requiring further validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".