Factors Influencing Pain Response Following Patient Education and Supervised Exercise in Male and Female Subjects With Hip Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To understand factors associated with pain intensity responder status following nonsurgical hip osteoarthritis (OA) intervention, according to sex. METHODS: Data were from individuals with hip OA participating in the Danish Good Life With Osteoarthritis in Denmark 8-week education and exercise program. The following factors were recorded at program entry: age; education; mental well-being; comorbidities; body mass index; symptoms in hip, knee, and low back; and program-specific factors including education sessions, former participant lectures, and supervised exercise sessions. Pain intensity was recorded at baseline and at month 3 (post-program) on a 0-100-mm visual analog scale. Response was defined as pain intensity improvement of ≥30% from baseline to post-program. Logistic regression was used and conducted separately in male and female subjects. RESULTS: The sample included 791 men and 2,253 women. Female subjects had a mean baseline pain score of 47.2 of 100 (95% confidence interval [95% CI] 46.4-48.1) and male subjects had a score of 41.7 (95% CI 40.3-43.1). By post-program, the proportion of pain responders was 50.4% among women and 45.8% among men (difference P = 0.025). Among women, program-specific factors (attending former participant lectures and more supervised exercise sessions) were positively associated with pain response, as were better mental well-being and fewer comorbidities, while symptoms in other joints/sites were associated with a decreased likelihood of response. Among men, program-specific factors were not associated with response, while better mental well-being and fewer comorbidities were associated with being a responder. CONCLUSION: Findings suggest that the influence of some factors on pain response differ for male and female subjects and point to a potential need for targeted approaches for men and women who may require different key messages/approaches from health care providers.
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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 | 0.000 | 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) | 0.002 | 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".