Comparing a daily versus weekly titration algorithm in people with type 2 diabetes switching from basal insulin to <scp>iGlarLixi</scp> in the <scp>LixiLan ONE CAN</scp> randomized trial
Bibliographic record
Abstract
AIM: To compare the efficacy and safety of a simple daily titration algorithm compared with a weekly dose adjustment of iGlarLixi in people with type 2 diabetes. MATERIALS AND METHODS: LixiLan ONE CAN (NCT03767543), a randomized, 26-week, open-label, multicentre phase 3 trial conducted in Canada, involved 265 people with type 2 diabetes and an HbA1c of ≥7.5% to ≤ 10.5% or less (≥58 to ≤91 mmol/mol) on basal insulin for 6 months or longer. Participants were randomized 1:1 with instructions to self-titrate iGlarLixi daily (1 unit/day) or once weekly (2 or 4 units/week) to a common target fasting plasma glucose of 4.4 to 5.6 mmol/L (79 to 101 mg/dl). The primary objective was to show non-inferiority of the daily versus weekly titration algorithm. RESULTS: At 26 weeks, daily titration of iGlarLixi was not inferior to a weekly titration for both the prespecified primary endpoint of change in HbA1c from baseline (least square [LS] mean change: -1.24% vs. -0.92%, respectively; LS mean difference: 0.32%; 95% CI [0.07, 0.57]; P < .0001) and for the secondary endpoint of change in weight from baseline (LS mean change: -0.22 vs. +0.81 kg, respectively; LS mean difference: 1.03 kg; 95% CI [0.01, 2.06]; P < .0001). Indeed, for both the primary and secondary outcome, the daily titration of iGlarLixi was superior. There were no statistically significant differences in hypoglycaemia incidence between the two titration strategies during the 26-week study. CONCLUSION: A daily titration algorithm for switching basal insulin to iGlarLixi was shown to be non-inferior and superior for glycaemic control and weight compared with weekly titration.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".