Safety and effectiveness of biphasic insulin aspart 30/70 (NovoMix<sup>®</sup>30) when switching from human premix insulin in patients with type 2 diabetes: subgroup analysis from the 6-month IMPROVE™ observational study
Bibliographic record
Abstract
AIMS: IMPROVE is an open-label, multinational, non-randomised, 26-week observational study designed to evaluate the safety and effectiveness of biphasic insulin aspart 30 (BIAsp 30) in routine clinical practice. Here, we report data for patients switching to BIAsp 30 from human premixed insulin. METHODS: Patients (n = 3856) with type 2 diabetes previously receiving human premixed insulin with or without oral antidiabetic drugs were eligible for inclusion. Demographic data, efficacy end-points (HbA(1c), fasting blood glucose and postprandial blood glucose) and safety end-points (serious adverse drug reactions, hypoglycaemia and adverse events) were collected at baseline and final visit. A subgroup analysis of mean dose change was also undertaken. RESULTS: Switching patients to BIAsp 30 resulted in significant improvements in glycaemic control combined with a reduced risk of hypoglycaemia. Patients who reached the HbA(1c) target (< 7%) had shorter diabetes duration, lower HbA(1c) at baseline and needed less insulin. Over 30% of patients were able to reach this target without experiencing hypoglycaemia over the 26-week period. Compared with asymmetric dose switching, unit-for-unit switching resulted in the highest proportion of patients reaching HbA(1c) target and incurred the least amount of dose titration. CONCLUSIONS: A unit-for-unit switch is the most effective as well as the simplest approach when transferring patients from biphasic human insulin 30 to BIAsp 30.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".