Can We Predict Those With Osteoarthritis Who Will Worsen Following a Chronic Disease Management Program?
Bibliographic record
Abstract
OBJECTIVE: To identify predictors of worsening symptoms and overall health of the treated hip or knee joint following 26 weeks of a nonsurgical chronic disease management program for hip and knee osteoarthritis (OA) and to examine the consistency of these predictors across 3 definitions of worsening. METHODS: This prospective cohort study followed 539 participants of the program for 26 weeks. The 3 definitions of worsening included symptomatic worsening based on change in the Western Ontario and McMaster Universities Osteoarthritis Index Global score (WOMAC-G) measuring pain, stiffness, and function; a transition scale that asked about overall health of the treated hip or knee joint; and a composite outcome including both. Multivariate logistic regression models were constructed for the 3 definitions of worsening. RESULTS: Complete data were available for 386 participants: mean age was 66.3 years, 69% were female, 85% reported knee joint pain as primary symptom (signal joint), 46% were waitlisted for total joint arthroplasty (TJA). TJA waitlist status, signal joint, 6-Minute Walk Test (6MWT), depressive symptoms, pain, and age were independently associated with at least 1 definition of worsening. TJA waitlist status and 6MWT remained in the multivariate models for the transition and composite definitions of worsening. CONCLUSION: Participants reporting worsening on the transition scale did not consistently meet the WOMAC-G definition of worsening symptoms. TJA waitlist status was predictive of the composite definition of worsening, a trend apparent for the transition definition. However, variables that predict worsening remain largely unknown. Further research is required to direct comprehensive and targeted management of patients with hip and knee OA.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".