Systematic Evaluation of the Quality of Randomized Controlled Trials in Diabetes
Bibliographic record
Abstract
OBJECTIVE: We sought to systematically ascertain the quality of randomized controlled trials (RCTs) in diabetes. RESEARCH DESIGN AND METHODS: We identified the 10 most recently published trials as of 31 October 2003 in each of six general medical, five diabetes, and five metabolism and nutrition journals and further enriched our sample with 10 additional RCTs from each of five journals that published the most eligible RCTs in a year. We explored the association between trial characteristics and reporting quality using univariate analyses and a preplanned multivariate regression model. RESULTS: After excluding redundant reports of included trials and one trial that measured outcomes on the health system and not on patients, we included 199 RCTs: 119 assessed physiological and other laboratory outcomes, 42 assessed patient-important outcomes (e.g., morbidity and mortality, quality of life), and 38 assessed surrogate outcomes (e.g., disease progression or regression, HbA(1c), cholesterol). Fifty-three percent were of low methodological quality, as were one-third (36-40%) of trials reporting patient-important or surrogate outcomes and two-thirds (64%) of laboratory investigations. Independent predictors of low quality were nonprofit funding source (odds ratio 3.1 [95% CI 1.5-6.2]), measure of physiological and laboratory outcomes (2.3 [1.2-4.4]), and cross-over design (2.3 [1.1-4.8]), all characteristics of laboratory clinical investigations. CONCLUSIONS: There is ample room for improving the quality of diabetes trials. To enhance the practice of evidence-based diabetes care, trialists need to pay closer attention to the rigorous implementation and reporting of important methodological safeguards against bias in randomized trials.
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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 | 0.572 | 0.856 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.018 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| 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; 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".