Quality improvement needed in quality improvement randomised trials: systematic review of interventions to improve care in diabetes
Bibliographic record
Abstract
OBJECTIVE: Despite the increasing numbers of published trials of quality improvement (QI) interventions in diabetes, little is known about the risk of bias in this literature. DESIGN: Secondary analysis of a systematic review. DATA SOURCES: Medline, the Cochrane Effective Practice and Organisation of Care (EPOC) database (from inception to July 2010) and references of included studies. ELIGIBILITY CRITERIA: Randomised trials assessing 11 predefined QI strategies or financial incentives targeting health systems, healthcare professionals or patients to improve the management of adult outpatients with diabetes. ANALYSIS: Risk of bias (low, unclear or high) was assessed for the 142 trials in the review across nine domains using the EPOC version of the Cochrane Risk of Bias Tool. We used Cochran-Armitage tests for trends to evaluate the improvement over time. RESULTS: There was no significant improvement over time in any of the risk of bias domains. Attrition bias (loss to follow-up) was the most common source of bias, with 24 trials (17%) having high risk of bias due to incomplete outcome data. Overall, 69 trials (49%) had at least one domain with high risk of bias. Inadequate reporting frequently hampered the risk of bias assessment: allocation sequence was unclear in 82 trials (58%) and allocation concealment was unclear in 78 trials (55%). There were significant reductions neither in the proportions of studies at high risk of bias over time nor in the adequacy of reporting of risk of bias domains. CONCLUSIONS: Nearly half of the included QI trials in this review were judged to have high risk of bias. Such trials have serious limitations that put the findings in question and therefore inhibit evidence-based QI. There is a need to limit the potential for bias when conducting QI trials and improve the quality of reporting of QI trials so that stakeholders have adequate evidence for implementation.
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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 | 0.188 | 0.461 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.027 | 0.024 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".