Predictors of Placebo Response to Local (<scp>Intra‐Articular</scp>) Therapy In Osteoarthritis: An Individual Participant Data <scp>Meta‐Analysis</scp>
Bibliographic record
Abstract
OBJECTIVE: We undertook this study to evaluate potential predictors of placebo response with intra-articular (IA) injections for knee/hip osteoarthritis (OA) using individual participant data (IPD) from existing trials. METHODS: Randomized placebo-controlled trials evaluating IA glucocorticoid or hyaluronic acid published to September 2018 were selected. IPD for disease characteristics and outcome measures were acquired. Potential predictors of placebo response included participant characteristics, pain severity, intervention, and trial design. Placebo response was defined as at least a 20% reduction in baseline pain. Logistic regression models and odds ratios were computed as effect measures to evaluate patient and pain mechanisms and then pooled using a random effects model. Generalized mixed-effect models were applied to intervention and trial characteristics. RESULTS: Of 56 eligible trials, 6 shared data, and these were combined with the existing 4 OA Trial Bank studies, yielding 10 studies with IPD of 621 placebo participants for analysis. In the total placebo population, at short-term follow-up, the use of local anesthetic and ultrasound guidance were associated with reduced odds of placebo response. At midterm follow-up, mid- to long-term trial duration was associated with increased odds of placebo response, and worse baseline function scores were associated with reduced odds of a placebo response. CONCLUSION: The administration of local anesthetics or ultrasound guidance may reduce IA placebo response at short-term follow-up. At midterm follow-up, participants with worse baseline function scores may be less likely to respond to IA placebo, and mid- to long-term trial duration may enhance the placebo response. Further studies are required to corroborate these potential predictors of IA placebo response.
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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 | 0.039 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.040 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".