Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis.
Bibliographic record
Abstract
CONTEXT: There have been numerous reports of interventions designed to improve the care of patients with diabetes, but the effectiveness of such interventions is unclear. OBJECTIVE: To assess the impact on glycemic control of 11 distinct strategies for quality improvement (QI) in adults with type 2 diabetes. DATA SOURCES AND STUDY SELECTION: MEDLINE (1966-April 2006) and the Cochrane Collaboration's Effective Practice and Organisation of Care Group database, which covers multiple bibliographic databases. Eligible studies included randomized or quasi-randomized controlled trials and controlled before-after studies that evaluated a QI intervention targeting some aspect of clinician behavior or organizational change and reported changes in glycosylated hemoglobin (HbA1c) values. DATA EXTRACTION: Postintervention difference in HbA1c values were estimated using a meta-regression model that included baseline glycemic control and other key intervention and study features as predictors. DATA SYNTHESIS: Fifty randomized controlled trials, 3 quasi-randomized trials, and 13 controlled before-after trials met all inclusion criteria. Across these 66 trials, interventions reduced HbA(1c) values by a mean of 0.42% (95% confidence interval [CI], 0.29%-0.54%) over a median of 13 months of follow-up. Trials with fewer patients than the median for all included trials reported significantly greater effects than did larger trials (0.61% vs 0.27%, P = .004), strongly suggesting publication bias. Trials with mean baseline HbA1c values of 8.0% or greater also reported significantly larger effects (0.54% vs 0.20%, P = .005). Adjusting for these effects, 2 of the 11 categories of QI strategies were associated with reductions in HbA(1c) values of at least 0.50%: team changes (0.67%; 95% CI, 0.43%-0.91%; n = 26 trials) and case management (0.52%; 95% CI, 0.31%-0.73%; n = 26 trials); these also represented the only 2 strategies conferring significant incremental reductions in HbA1c values. Interventions involving team changes reduced values by 0.33% more (95% CI, 0.12%-0.54%; P = .004) than those without this strategy, and those involving case management reduced values by 0.22% more (95% CI, 0.00%-0.44%; P = .04) than those without case management. Interventions in which nurse or pharmacist case managers could make medication adjustments without awaiting physician authorization reduced values by 0.80% (95% CI, 0.51%-1.10%), vs only 0.32% (95% CI, 0.14%-0.49%) for all other interventions (P = .002). CONCLUSIONS: Most QI strategies produced small to modest improvements in glycemic control. Team changes and case management showed more robust improvements, especially for interventions in which case managers could adjust medications without awaiting physician approval. Estimates of the effectiveness of other specific QI strategies may have been limited by difficulty in classifying complex interventions, insufficient numbers of studies, and publication bias.
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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 | 0.044 | 0.076 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.074 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".