Similar glycaemic control and less hypoglycaemia during active titration after insulin initiation with glargine 300 units/mL and degludec 100 units/mL: A subanalysis of the BRIGHT study
Bibliographic record
Abstract
AIM: To further investigate glycaemic control and hypoglycaemia in BRIGHT, focusing on the titration period. MATERIALS AND METHODS: BRIGHT was a multicentre, open-label, randomized, active-controlled, two-arm, parallel-group, 24-week study in insulin-naïve patients with uncontrolled type 2 diabetes initiated on glargine 300 U/mL (Gla-300) (N = 466) or degludec (IDeg-100) (N = 463). Predefined efficacy and safety outcomes were investigated during the initial 12-week titration period. In addition, patients' characteristics and clinical outcomes were assessed descriptively, stratified by confirmed (≤3.9 mmol/L) hypoglycaemia incidence during the initial titration period. RESULTS: At week 12, HbA1c was comparable between Gla-300 (7.32%) and IDeg-100 (7.23%), with similar least squares (LS) mean reductions from baseline (-1.37% and - 1.39%, respectively; LS mean difference of 0.02; 95% confidence interval: -0.08 to 0.12). Patients who experienced hypoglycaemia during the initial titration period had numerically greater HbA1c reductions by week 12 than patients who did not (-1.46% vs. -1.28%), and higher incidence of anytime (24 hours; 73.3% vs. 35.7%) and nocturnal (00:00-06:00 hours; 30.0% vs. 11.9%) hypoglycaemia between weeks 13-24. CONCLUSIONS: The use of Gla-300 resulted in similar glycaemic control as IDeg-100 during the initial 12-week titration period of the BRIGHT study, when less anytime (24 hours) hypoglycaemia with Gla-300 versus IDeg-100 has been reported. Experiencing hypoglycaemia shortly after initiating Gla-300 or IDeg-100 may be associated with hypoglycaemia incidence in the longer term, potentially impacting glycaemic 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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".