Effectiveness of disease-management programs for improving diabetes care: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: We conducted a meta-analysis of randomized controlled trials to assess the effectiveness of disease-management programs for improving glycemic control in adults with diabetes mellitus and to study which components of programs are associated with their effectiveness. METHODS: We searched several databases for studies published up to December 2009. We included randomized controlled trials involving adults with type 1 or 2 diabetes that evaluated the effect of disease-management programs on glycated hemoglobin (hemoglobin A₁(C)) concentrations. We performed a meta-regression analysis to determine the effective components of the programs. RESULTS: We included 41 randomized controlled trials in our review. Across these trials, disease-management programs resulted in a significant reduction in hemoglobin A₁(C) levels (pooled standardized mean difference between intervention and control groups -0.38 [95% confidence interval -0.47 to -0.29], which corresponds to an absolute mean difference of 0.51%). The finding was robust in the sensitivity analyses based on quality assessment. Programs in which the disease manager was able to start or modify treatment with or without prior approval from the primary care physician resulted in a greater improvement in hemoglobin A₁(C) levels (standardized mean difference -0.60 v. -0.28 in trials with no approval to do so; p < 0.001). Programs with a moderate or high frequency of contact reported a significant reduction in hemoglobin A₁(C) levels compared with usual care; nevertheless, only programs with a high frequency of contact led to a significantly greater reduction compared with low-frequency contact programs (standardized mean difference -0.56 v. -0.30, p = 0.03). INTERPRETATION: Disease-management programs had a clinically moderate but significant impact on hemoglobin A₁(C) levels among adults with diabetes. Effective components of programs were a high frequency of patient contact and the ability for disease managers to adjust treatment with or without prior physician approval.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.067 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.077 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".