Comparison of Insulin Titration Strategies for Glycemic Control in Type 2 Diabetes: A Systematic Review and Network Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Adjusting basal insulin doses is essential for lowering blood glucose while minimizing the risk of hypoglycemia. Despite various basal insulin titration strategies being available, their comparative effectiveness remains unclear. PURPOSE: To compare the effectiveness of different basal insulin titration strategies on glycemic control in patients with type 2 diabetes. DATA SOURCES: PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL, and EBSCO Open Dissertations were searched from inception to January 2024. STUDY SELECTION: We included published trials with evaluation of basal insulin titration strategies for glycemic control in type 2 diabetes. DATA EXTRACTION: Data on HbA1c and severe hypoglycemia were extracted. DATA SYNTHESIS: Studies were categorized with the theme, intensity, and provider/platform (TIP) framework. "Theme" referred to conventional titration (Conv) or self-titration (ST), "intensity" was categorized as high (Conv, >1/month; ST, ≥2/week) or low (Conv, ≤1/month; ST, <2/week), and for "provider/platform" categories included supported by health care provider (HCP for Conv or S-HCP for ST), patient led (Pt), and supported by application (S-App). The ST/High/S-HCP strategy resulted in the greatest HbA1c reduction in comparison with all others (e.g., ST/High/S-App, mean difference -0.75 [95% CI -1.26, -0.25], and Conv/Low/HCP, -1.19 [95% CI -1.67, -0.72]). Severe hypoglycemia risk did not differ significantly across strategies. LIMITATIONS: The number of studies per network meta-analysis was limited, and not all TIP combinations were evaluated. CONCLUSIONS: Self-titration at least twice a week with health care provider support leads to superior HbA1c reduction in comparison with other strategies, without increasing the risk of severe hypoglycemia. This approach should be considered for clinical practice, where appropriate, to achieve optimal glycemic control in patients with type 2 diabetes.
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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".