Initiating insulin: How to help people with type 2 diabetes start and continue insulin successfully
Bibliographic record
Abstract
With the growing incidence of type 2 diabetes worldwide, healthcare professionals (HCPs) find an increasing proportion of their time devoted to the management of diabetes.Because this condition is chronic and characterised by progressive decreases in insulin secretion and sensitivity, insulin therapy becomes necessary for a large number of persons with type 2 diabetes.1 However, despite the efficacy of insulin treatment, patients and their HCPs continue to be challenged with reaching glycaemic goals and maintaining long-term insulin therapy.Asmanyas30%-50%ofpatientsmayremainabovetheirglucose target 6 months after starting basal insulin.2,3 One key contributor is problematic patient adherence.In this paper, we consider what factors may be critical to encouraging successful insulin initiation and promoting long-term treatment maintenance, and we provide practical tips to help with this important transition, particularly in the current environment of limited time and resources.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.028 | 0.018 |
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".