Knee OA: which patients are unlikely to benefit from manual PT and exercise?
Bibliographic record
Abstract
BACKGROUND: The combination of manual physical therapy and exercise provides important benefit for more than 80% of patients with knee osteoarthritis (OA). Our objective was to determine predictor variables for patients unlikely to respond to these interventions. METHODS: We used a retrospective combined cohort study design to develop a preliminary clinical prediction rule (CPR). To determine useful predictors of nonsuccess, we used an extensive set of 167 baseline variables. These variables were extracted from standardized examination forms used with 101 patients(64 women and 37 men with a mean age of 60.5}11.8 and 63.6}9.3 years, respectively) in 2 previously published clinical trials. We classified patients based on whether they achieved a clinically meaningful benefit of at least 12%improvement in Western Ontario MacMaster(WOMAC) scores after 4 weeks of treatment using the smallest and most efficient subset of predictors. RESULTS: The variables of patellofemoral pain, anterior cruciate ligament laxity, and height >1.71 m (5’7’’) comprise the CPR. Patients with at least 2 positive tests yield eda posttest probability of 88% for nonsuccess with this treatment (positive likelihood ratio=36.7). The overall prognostic accuracy of the CPR was 96%. CONCLUSION: Most patients with knee OA will benefit from a low-risk, cost-effective program of manual physical therapy and supporting exercise.1,2 The few patients who may not benefit from such a program are identifiable by a simple (preliminary) CPR. After validation,this rule could improve primary patient management,allowing more appropriate referrals and choices in intervention.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".