Factors influencing initial choice of insulin therapy in a large international non‐interventional study of people with type 2 diabetes
Bibliographic record
Abstract
AIM: To use baseline characteristics of the Cardiovascular Risk Evaluation in people with type 2 Diabetes on Insulin Therapy study population to identify factors that could explain the choice of insulin therapy when beginning insulin. METHODS: The source, non-interventional, longitudinal, long-term study involves 314 centres in 12 countries in five regions. People were enrolled having started any insulin regimen in the previous 12 months. To identify factors associated with the choice of insulin regimen, multivariable backward logistic regression was performed on eligible physician and participant explanatory variables. RESULTS: Participants (N = 3031) had mean age 62 years, diabetes duration 11 years, body mass index 29.3 kg/m² and an HbA1c of 9.5%. Participants in Japan had less hypertension, smoked more and used fewer concomitant medications than those of other regions. Only physician location (rural or urban) influenced the choice of insulin in Japan. In the other four-regions-combined, physician location, specialty, sex and practice type influenced choice of insulin as did participant location, baseline HbA1c, use of glucose-lowering therapies and prior insulin secretagogue use. CONCLUSION: Choice of initial insulin regimen was influenced by several physician and participant characteristics in Canada and Europe, but only by physician location in Japan.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".