A survey of physician experience and treatment satisfaction using fast-acting insulin aspart in people with type 1 or type 2 diabetes
Bibliographic record
Abstract
This survey aimed to explore real-world physician experiences and treatment satisfaction with fast-acting insulin aspart (faster aspart) in clinical practice across Europe and Canada. An online web-based survey was used for physicians treating people with type 1 or type 2 diabetes. General practitioners and specialists, with experience using faster aspart, were interviewed. A total of 191 physicians participated in the survey. Most of their patients (68% of those with T1D and 63% of those with T2D) were previously treated with another mealtime insulin before switching to faster aspart. At the time of initiating faster aspart, nearly half of patients had an HbA1c level between 7.5% (59 mmol/mol) and 8.5% (69 mmol/mol). The main prescription drivers for faster aspart, versus other mealtime insulins, were faster onset of action, improved postprandial glucose (PPG) control, and dosing flexibility. Most physicians were more satisfied with faster aspart than other mealtime insulins regarding at-meal (66%) and post-meal (71%) dosing flexibility, improved PPG levels (66%), and onset of action (61%). Main reasons for not prescribing faster aspart included a good response to current treatment (76%) or patient reluctance to switch (57%). Overall, 12% of patients discontinued faster aspart, for reasons including concerns of hypoglycemia (17%), poor adherence (17%), and level of patient co-pay (17%). More than half of physicians had fewer concerns regarding postprandial hyperglycemia, and were more confident in their patients reaching their HbA1c target with faster aspart than with other mealtime insulins. The findings of this survey are based heavily on physicians’ experiences, and could therefore be subject to recall bias. Reported physician and patient experiences of using faster aspart have been positive, and better PPG control and increased dosing flexibility are expected to improve glycemic management.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".