Pain and Its Management: Strategies and Outcomes in Older Adults With or at Risk for Knee Osteoarthritis
Bibliographic record
Abstract
Abstract Knee osteoarthritis (KOA) is a leading cause of mobility disability that is characterized by chronic pain among older adults. Non-Hispanic Blacks (NHBs) suffer disproportionately from non-Hispanic Whites (NHWs), reporting higher pain intensity and disability. It is unclear how these differences in symptomatology translate into different patterns of utilization for self-management (SM) of pain, and if such patterns are associated with underlying biological pain mechanisms. This multisite observational study examined (1) use of self-management strategies among older NHB and NHW adults with/at risk for KOA and (2) associations among self-management strategies, clinical and experimental pain. After approval from institutional IRBs, NHB and NHW older adults (N= 202) with knee pain completed the McGill Pain Questionnaire-Short Form, questions on treatment strategies (e.g., massage, ice, heat, medications), and quantitative sensory testing. Covariates included study site and education. On average, participants reported using 2 ± 1.65 SM strategies, with 79% endorsing at least one SM strategy. Analysis of covariance revealed that clinical pain differed by race/ethnicity and use of SM and/or medical treatments (p’s < 0.01). SM use did not differ by race/ethnicity, p = 0.15, but did differ significantly by gender, p < 0.05. Multiple linear regression demonstrated significant positive associations between SM and heat pain sensitivity for both NHBs and NHWs, (p < 0.05). SM is an important component of OA management for NHBs and NHWs. Our study is one of the first to show that SM use is significantly associated with pain mechanisms. Improved understanding will facilitate better mechanism-targeted pain management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".