Comparison of the Effect of Glycemic Control in Type 2 Diabetes Outpatients Treated With Premixed and Basal Insulin Monotherapy in China
Bibliographic record
Abstract
Background: Basal and premixed insulin have been widely used for insulin therapy of type 2 diabetes mellitus (T2DM) in China. The aim of this study is to compare the sustained efficacy of basal and premixed insulin therapies in T2DM outpatients with insulin monotherapy. Materials and Methods: The survey was conducted in 602 hospitals across China from April to June in 2013. The participants included outpatients who were receiving basal or premixed insulin monotherapy for more than 3 months, and the outcome was attaining a glycated hemoglobin A1C (HbA1c) of <7.0% as a measure of sustained glycemic control. Results: A total of 49,119 T2DM outpatients on basal (n=11,967) or premixed insulin (n=37,152) monotherapy were included in the final analyses. Using multivariable model analysis, patients using premixed insulin exhibited a better glycemic control, with more outpatients achieving the target HbA1c level than those using basal insulin (model 1, OR 0.695, 95%CI 0.664-0.728; model 2, OR 0.708, 95%CI 0.676-0.742; model 3, OR 0.717, 95%CI 0.684-0.752; model 4, OR 0.750, 95%CI 0.715-0.787). Using subgroup analysis stratified by age, sex, duration of diabetes, duration of insulin treatment and complications, still more outpatients in every subgroup treated with premixed insulin achieved the target HbA1c (HbA1c<7%) than those receiving basal insulin. Conclusions: Premixed insulin monotherapy had a better glycemic control (HbA1c<7.0%) than basal insulin monotherapy for Chinese T2DM outpatients in daily.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".