Time‐in‐range derived from self‐measured blood glucose in people with type 2 diabetes advancing to <scp>iGlarLixi</scp> : A participant‐level pooled analysis of three phase 3 <scp>LixiLan</scp> randomized controlled trials
Bibliographic record
Abstract
AIM: To evaluate the efficacy of a fixed-ratio combination of insulin glargine 100 U/mL plus lixisenatide (iGlarLixi) in people with type 2 diabetes (T2D) using derived time-in-range (dTIR). METHODS: Participant-level data from LixiLan-L, LixiLan-O and LixiLan-G were pooled and dTIR (70-180 mg/dL), derived time-above-range (> 180 mg/dL) and derived time-below-range (dTBR; < 70 mg/dL) were calculated from participant seven-point self-monitored blood glucose profiles. RESULTS: This pooled analysis included data from 2420 participants receiving iGlarLixi (n = 1093), iGlar (n = 836), Lixi (n = 234) or a glucagon-like peptide-1 receptor agonist (GLP-1 RA) (n = 257). Numerically greater improvements in least square (LS) means dTIR were seen from baseline to end of treatment (EOT) with iGlarLixi (25.7%) versus iGlar (15.8%), Lixi (11.7%) or GLP-1 RA (16.2%). At EOT, the mean (standard deviation) dTBR was 0.71% ± 3.4%, 0.61% ± 3.2%, 0.08% ± 1.0% and 0.0% ± 0.0% for iGlarLixi, iGlar, Lixi and GLP-1 RA, respectively. In a subgroup analysis, participants aged younger than 65 years (n = 1690) and 65 years or older (n = 713) showed numerically greater improvements in LS means dTIR from baseline to EOT with iGlarLixi versus iGlar, Lixi or GLP-1 RA. CONCLUSIONS: iGlarLixi achieved improvements in dTIR, with low dTBR values, providing further evidence to inform clinical outcomes with the use of iGlarLixi.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".