Examining patient activation and other factors associated with changes in pain and function following best evidence osteoarthritis care
Bibliographic record
Abstract
Objectives: The primary objective was to examine baseline patient activation as a prognostic factor for changes in pain and function following participation in an osteoarthritis management program. The secondary objective was to examine other prognostic factors from existing literature (e.g. employment, functional performance, depression, comorbidities). Method: One-hundred-and-eleven participants with knee osteoarthritis were assessed at 0-, 12- and 26-weeks in this prospective clinical cohort. Demographic variables, timed-up-and-go (TUG), patient activation measure (PAM-13), Depression Anxiety Stress Scale and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were collected. Multivariable linear regression examined relationships between prognostic factors and pain and function at 12- and 26-weeks. Results: Complete 12- and 26-week data were available for 89 and 74 participants respectively, 66 % female, 66.8 (SD 10.0) years, 74 % unemployed, 66 % finished high school or higher, 12 % on joint arthroplasty waitlists. Baseline PAM-13 scores were not associated with changes in pain or function at 12- or 26-weeks. Employment status (β = 9.17 (95 % CI 2.11, 16.24), p = 0.01) and TUG (β = -1.20 (95 % CI -1.91, -0.49), p < 0.01) were associated with changes in pain at week-12. Employment status (β = 11.60 (95 % CI 5.31, 17.90), p < 0.01) and TUG (β = -1.10 (95%CI -1.78, -0.43), p < 0.01) were associated with 12-week function. Baseline TUG (β = -1.32 (95 % CI -2.40, -0.23), p = 0.02) was associated with week-26 WOMAC function. Conclusions: Baseline PAM-13 scores were not associated with changes in pain and function at any timepoint. Employment status and TUG were associated with changes in pain and function at 12-weeks, TUG was associated with 26-week function.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".