Hypoglycaemia as a function of HbA1c in type 2 diabetes: Insulin glargine 300 U/mL in a patient‐level pooled analysis of EDITION 1, 2 and 3
Bibliographic record
Abstract
Basal insulin therapy often involves a compromise between achievement of glycaemic targets and avoidance of hypoglycaemia, dependent on how intensively insulin is titrated. In the Phase 3a EDITION 1, 2 and 3 studies, insulin glargine 300 U/mL (Gla-300) provided glycaemic control equivalent to that of insulin glargine 100 U/mL (Gla-100), with less hypoglycaemia in individuals with type 2 diabetes mellitus (T2DM). The current study evaluated the rates of confirmed (≤3.9 mmol/L [≤70 mg/dL]) or severe hypoglycaemia over six months of treatment with Gla-300 or Gla-100 in the EDITION studies, as a function of HbA1c. Analysis was performed on patient-level data pooled from the three EDITION studies, and annualized hypoglycaemia rate as a function of HbA1c at Month 6 was fitted using a negative binomial regression model. Participants treated with Gla-300 experienced a consistently lower rate of confirmed (≤3.9 mmol/L [≤70 mg/dL]) or severe hypoglycaemia as compared with those treated with Gla-100, regardless of HbA1c at Month 6. Results suggest that treatment with Gla-300 vs Gla-100 could allow individuals with T2DM to achieve equivalent glycaemic control with less hypoglycaemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.043 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".