The importance of the initial period of basal insulin titration in people with diabetes
Bibliographic record
Abstract
Achieving target glycaemic control is essential in people with diabetes to minimize the risk of long-term complications, and many people with type 2 diabetes will ultimately require basal insulin (BI) therapy to achieve their individualized glycaemic targets. Usually, the first 12 weeks following initiation of BI therapy represents the period when the greatest dose increases and glycaemic reductions occur. Effective glycaemic control combined with minimizing the risk of hypoglycaemia is important to enable the achievement of glycaemic control in the longer term. However, substantial therapeutic inertia exists in clinical practice, both in initiation and up-titration of BI, owing to patient-, physician- and healthcare system-related barriers, including fear of hypoglycaemia and the perception of a burdensome regimen. The more prolonged duration of action, reduced glycaemic variability and lower risk of hypoglycaemia seen with second-generation versus first-generation BI analogues may help alleviate patients' and physicians' concerns and facilitate titration. In turn, optimal BI titration and subsequent metabolic benefits may help improve therapy adherence and self-management. This review details the clinical implications of prompt titration of BI to achieve early glycaemic control, and the importance of minimizing hypoglycaemia risk within the initial titration period. Facilitation of patients' self-management of BI is also addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".