Outcome Domains, Outcome Measures, and Characteristics of Randomized Controlled Trials Testing Nonsurgical Interventions for Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Core outcome set (COS) is the minimum set of outcome domains that should be measured and reported in clinical trials. We analyzed outcome domains, prevalence of use of COS published by Outcome Measures in Rheumatology (OMERACT) initiative, outcome measures for outcome domains recommended by OMERACT COS, duration and size of randomized controlled trials (RCT) testing nonsurgical interventions for osteoarthritis (OA). METHODS: We searched PubMed and analyzed RCT about nonsurgical interventions for OA published from June 2012 to June 2017. We extracted data about trial type, use of OMERACT COS, efficacy outcome domains, safety outcome domains, outcome measures used for COS assessment, duration, and sample size. RESULTS: Among 334 analyzed trials, complete OMERACT-recommended COS was used by 14% of trials. Higher median prevalence of using OMERACT COS was found in trials explicitly described as phase III, and trials of pharmacological interventions with followup ≥ 1 year, but both with wide range of COS usage. Trialists used numerous different outcome measures for analyzing core outcome domains: 50 different outcome measures for pain, 74 for physical function, 9 for patient's global assessment, and 5 for imaging. CONCLUSION: Suboptimal use of recommended COS and heterogeneity of outcome measures is reducing quality and comparability of OA trials and hinders conclusions about efficacy and comparative efficacy of nonsurgical interventions. Interventions for improving study design of trials in this field would be beneficial.
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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 | 0.271 | 0.551 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".