A real-world study assessing the efficacy and safety of switching from basal bolus insulin therapy to once daily iGlarLixi in people with type 2 diabetes mellitus: soli de-escalation
Bibliographic record
Abstract
AIM: To compare glycemic outcomes in adults with type 2 diabetes mellitus (T2DM) switching from basal-bolus insulin (BBI) to once-daily iGlarLixi. MATERIALS AND METHODS: This real-world retrospective observational study analyzed electronic medical records of adults (≥18 years) with T2DM who switched from BBI to iGlarLixi (01/01/2017-30/09/2022) from the US Optum Market Clarity® database. Primary endpoint was HbA1c change at 6 months after switching to iGlarLixi. Secondary endpoints included HbA1c change at 3 months and body weight change and hypoglycemic events at 6 months. Subgroup analyses included individuals aged ≥65 years and those with baseline HbA1c ≥7 % (53 mmol/mol). RESULTS: Participants (N = 372, mean age: 58.9 years; ≥65 years old: 30.6 %; females: 60.5 %) showed significant HbA1c reduction (0.9 %; p < 0.0001) from 9.6 % at baseline to 8.6 % at 6 months. In participants ≥65 years and those with HbA1c ≥7 %, HbA1c decreased from 9.3 % and 9.8 % to 8.4 % and 8.8 % at 6 months, respectively. The proportion of participants achieving HbA1c <7 % (53 mmol/mol) doubled from 7.0 % to 14.8 % at 6 months after switching to iGlarLixi. Hypoglycemia incidence and event rates decreased across all populations. CONCLUSIONS: Switching from BBI to once-daily iGlarLixi improved glycemic control with significant HbA1c reduction and fewer hypoglycemic events.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".