Insulin glargine 300 U/<scp>mL</scp> and insulin degludec: A review of the current evidence comparing these two second‐generation basal insulin analogues
Bibliographic record
Abstract
For most people with type 2 diabetes (T2D), treatment intensification with the addition of basal insulin therapy is required to maintain glycaemic control. However, this often does not happen in real-life practice promoting the development of long-term diabetes-related complications. The second-generation basal insulin analogues glargine 300 U/mL (Gla-300) and degludec (IDeg) provide pharmacokinetic and pharmacodynamic improvements that may allow them to be more effective in appropriately managing diabetes compared with first-generation basal insulin analogues. Both Gla-300 and IDeg have been extensively studied vs the first-generation basal insulin glargine 100 U/mL, demonstrating comparable efficacy in terms of glycaemic control, and a lower risk of hypoglycaemia. The BRIGHT randomized controlled trial is the first direct comparison of the efficacy and safety profiles of Gla-300 and IDeg in patients with T2D. Moreover, real-world data have been used to assess the effectiveness of these basal insulins during routine clinical practice. Further research is required to determine if the properties of Gla-300 and IDeg may lead to improvements in healthcare-related costs and the quality of life of patients, which are important factors for informing clinical decisions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".