The Quality of Randomized Controlled Trials in High-impact Rheumatology Journals, 1998–2018
Bibliographic record
Abstract
OBJECTIVE: Well-designed randomized controlled trials (RCT) mitigate bias and confounding, but previous evaluations of rheumatology trials found high rates of methodological flaws. Outside of rheumatoid arthritis, no studies in the modern era have assessed the quality of rheumatology RCT over time or regarding industry funding. METHODS: We identified all RCT published in 3 high-impact rheumatology journals from 1998, 2008, and 2018. Quality metrics derived from a modified Jadad scale were analyzed by year of publication and by funding source. RESULTS: Ninety-six publications met inclusion criteria; 82 of these described the primary analysis of an RCT. Over time (1998-2008-2018), trials were less likely to adequately report dropouts and withdrawals (100% vs 82% vs 60%; p < 0.01) or include an active comparator (44% vs 12% vs 13%; p = 0.01). Later trials were more likely to evaluate biologic therapy (11% vs 38% vs 83%; p < 0.01) and report adequate randomization procedures (39% vs 29% vs 60%; p = 0.04). Seventy-nine percent of trials received industry funding. Industry-funded trials were more likely to report double-blinding (86% vs 53%; p < 0.01), patient-reported outcome measures (77% vs 41%; p < 0.01), and intention-to-treat analyses (86% vs 65%; p = 0.04). CONCLUSION: Industry-funded trials comprise the majority of RCT published in high-impact rheumatology journals and more frequently report metrics associated with RCT quality. RCT assessing active comparators and nonbiologic therapies have become less common in high-impact rheumatology journals.
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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 | 0.510 | 0.832 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.028 | 0.038 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".