Examining the Relationships Among Treatment, Pain, and Physical Function in Patients With Osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: To better understand the relationships among treatment, pain, and physical function (PF). METHODS: Data were collected from 2 published randomized clinical trials of osteoarthritis patients who received tanezumab or a placebo. PF was measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) PF domain. Pain (WOMAC pain domain) was a mediator of the effect of treatment on PF. A set of mediation models were investigated. Variables were treatment (tanezumab vs placebo), WOMAC pain domain, and WOMAC PF domain. Cross-sectional mediation models were assessed separately at different weeks. Longitudinal mediation models used data from all weeks simultaneously. Results could identify a steady-state period. RESULTS: The cross-sectional and longitudinal mediation models showed a stable indirect effect of treatment through the pain on PF across time, indicating that a pseudo-steady-state model was appropriate. Therefore, the longitudinal steady-state mediation models were used with all available data assuming relationships among variables in the model being the same at all time points; results showed that the indirect effect of the treatment on PF was 77.8% in study 1 (NCT02697773) and 74.1% in study 2 (NCT02709486), both P <0.0001, whereas the direct effect was 22.2% for study 1 ( P = 0.0003) and 25.9% for study 2 ( P = 0.0019). DISCUSSION: At least 75% of the treatment effect of tanezumab on physical functioning can be explained by the improvements in pain. However, tanezumab had an additional effect on physical functioning (~25%) that, was independent of improvements in pain. Such independent effects are of considerable interest and require further research to determine their mechanisms.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".