Patient treatment satisfaction after switching to NovoMix® 30 (BIAsp 30) in the IMPROVE™ study: an analysis of the influence of prior and current treatment factors
Bibliographic record
Abstract
PURPOSE: Understanding treatment satisfaction (TS) for diabetes is increasingly important as treatment options increase. This study examines treatment satisfaction with NovoMix® 30 in an observational study in patients with type 2 diabetes. METHODS: The DiabMedSat assesses Overall, Treatment Burden, Symptom and Efficacy Treatment Satisfaction. The impact of type of pretreatment variables on TS was examined by ANOVA at baseline and week 26. Satisfaction at week 26 was examined by t-test and effect size. Linear regression models examined impact of prior treatment factors (age, gender, duration of diabetes, type of prior treatment and diabetes-related comorbidities) and current treatment factors (weight gain, hypoglycemic events, reaching therapeutic goal) on TS. RESULTS: The data set comprised 17,488 persons. Prior treatment with insulin had a more positive impact on baseline satisfaction. At week 26, there were no differences between type of prior treatment groups in Overall, Symptoms and Burden TS. Current treatment with NovoMix 30 significantly improved TS. Regression analyses examining the combined effect of pretreatment factors and current treatment factors found that all factors except for age-impacted TS although the domains impacted varied. CONCLUSIONS: Patients treated with NovoMix 30 reported improved treatment satisfaction, and the improvement is considered clinically meaningful to patients.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".