Real-World Health Outcomes of Insulin Glargine 300 U/mL vs Insulin Glargine 100 U/mL in Adults With Type 1 and Type 2 Diabetes in the Canadian LMC Diabetes Patient Registry: The REALITY Study
Bibliographic record
Abstract
OBJECTIVES: This study evaluated real-world clinical outcomes of patients with type 1 diabetes (T1D) and type 2 diabetes (T2D) initiating or transferring to insulin glargine 300 U/mL (Gla-300) vs insulin glargine 100 U/mL (Gla-100). METHODS: This is a retrospective cohort study using data from the Canadian LMC Diabetes Patient Registry. The 4 following cohorts were analyzed: 1) insulin-naïve patients with T2D who initiated Gla-300 or Gla-100, 2) patients with T2D who switched from neutral protamine Hagedorn (NPH) or detemir to Gla-300 or Gla-100, 3) patients with T2D who switched from Gla-100 to Gla-300 and 4) patients with T1D who switched from Gla-100, NPH or detemir to Gla-300. RESULTS: Of 376 propensity score-matched insulin-naïve patients, 6-month reduction in glycated hemoglobin (A1C) was similar between Gla-300 (-1.78%±1.85%; p<0.001) and Gla-100 (-1.74%±1.87%; p<0.001). In 114 propensity score-matched patients who switched from NPH or detemir, 6-month reduction in A1C was similar between Gla-300 (-0.78%±1.14%) and Gla-100 (-0.70%±1.57%). The 396 patients who switched from Gla-100 to Gla-300 had a significant reduction in A1C (-0.45%±1.39%; p<0.001). In 196 patients with T1D who switched from Gla-100, NPH or detemir to Gla-300, there was a significant reduction in A1C of -0.17%±1.19% (p=0.04). CONCLUSIONS: In a real-world clinical setting, insulin-naïve patients who initiated Gla-300 or Gla-100 showed similar changes in A1C and weight. Patients with T1D or T2D using Gla-300 transferred from another basal insulin had significant reductions in A1C with no change in weight or insulin dose.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".