The IMPROVE™ study - a multinational, observational study in type 2 diabetes: baseline characteristics from eight national cohorts
Bibliographic record
Abstract
AIMS: The IMPROVE study is a multinational, open-label, non-randomised, 26-week observational study assessing the safety and effectiveness of biphasic insulin aspart 30 (BIAsp 30) treatment in type 2 diabetes in routine clinical practice. The principal aims of this report were to characterise the baseline population and physicians' treatment decisions. METHODS: Patients with type 2 diabetes who required insulin and whose physician had decided to initiate BIAsp 30 were eligible. At baseline, demographic data and detailed medical histories were collected and physicians recorded their reasons for starting BIAsp 30, the glycaemic targets set and the regimens chosen. RESULTS: Data from 51,286 patients were included in analyses. Baseline glycaemic control was poor in all eight countries in the present analysis and in all prestudy treatment groups [no therapy, oral antidiabetic drugs (OADs) only, insulin with or without OADs], and the rates of vascular complications were high. Although the management of each of the three main measures of glycaemic control were key reasons for starting BIAsp 30, target-setting for postprandial glucose levels was variable. A twice-daily regimen was used to start BIAsp 30 therapy for 80% or more of patients. CONCLUSIONS: The IMPROVE baseline data reaffirm the global nature of poor glycaemic control in type 2 diabetes and echo the concerns that initiation of therapy, particularly insulin, is commonly delayed in clinical practice. Although postprandial glucose control was a key driver for physicians' choice of BIAsp 30, this was not consistently reflected in the targets set.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".