Application of Heterogeneity of <scp>Treatment‐Effects</scp> Methods: Exploratory Analyses of a Trial of <scp>Exercise‐Based</scp> Interventions for Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate heterogeneity of treatment effects in a trial of exercise-based interventions for knee osteoarthritis (OA). METHODS: Participants (n = 350) were randomized to standard physical therapy (PT; n = 140), internet-based exercise training (IBET; n = 142), or wait list (WL; n = 68) control. We applied qualitative interaction trees (QUINT), a sequential partitioning method, and generalized unbiased interaction detection and estimation (GUIDE), a regression tree approach, to identify subgroups with greater improvements in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score over 4 months. Predictors included 24 demographic, clinical, and psychosocial characteristics. We conducted internal validation to estimate optimism (bias) in the range of mean outcome differences among arms. RESULTS: Both QUINT and GUIDE indicated that for participants with lower body mass index (BMI), IBET was better than PT (improvements of WOMAC ranged from 6.3 to 9.1 points lower), and for those with higher BMI and a longer duration of knee OA, PT was better than IBET (WOMAC improvement was 6.3 points). In GUIDE analyses comparing PT or IBET to WL, participants not employed had improvements in WOMAC ranging from 1.8 to 6.8 points lower with PT or IBT versus WL. From internal validation, there were large corrections to the mean outcome differences among arms; however, after correction, some differences remained in the clinically meaningful range. CONCLUSION: Results suggest there may be subgroups who experience greater improvement in symptoms from PT or IBET, and this finding could guide referrals and future trials. However, uncertainty persists for specific treatment-effects size estimates and how they apply beyond this study sample.
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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 | 0.357 | 0.487 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.021 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".