A meta‐analysis of rate ratios for nocturnal confirmed hypoglycaemia with insulin degludec vs. insulin glargine using different definitions for hypoglycaemia
Bibliographic record
Abstract
AIMS: A prospective meta-analysis of phase 3 trials showed lower rates of nocturnal hypoglycaemia with insulin degludec vs. insulin glargine. We investigated the consistency of the results across different definitions of hypoglycaemia. METHODS: This post-hoc, patient-level meta-analysis included six randomized, controlled, 26- or 52-week phase 3a trials in insulin-naïve participants with Type 2 diabetes mellitus (Type 2 diabetesinsulin naïve ), participants with Type 2 diabetes mellitus using basal-bolus therapy (Type 2 diabetesBB ) and those with Type 1 diabetes mellitus. We used three definitions of hypoglycaemia and different timescales for the nocturnal period. Rates were analysed for the entire core trial period, the 'maintenance period' only, and the extension trial set population. Analyses utilized a negative binomial regression model. RESULTS: In Type 2 diabetesinsulin naïve participants, risk of nocturnal hypoglycaemia was significantly lower with insulin degludec vs. insulin glargine for all hypoglycaemia definitions and trial periods. Risk was also lower for the timescale 21.59-05.59, but not 00.01-07.59. For Type 2 diabetesBB , nocturnal hypoglycaemia rates were lower with insulin degludec vs. insulin glargine across all definitions, timescales and trial periods, with one exception. For individuals with Type 1 diabetes mellitus, nocturnal hypoglycaemia risk was significantly lower with insulin degludec during the maintenance period for the original definition (plasma glucose < 3.1 mmol/l, timescale 00.01-05.59) and in the extension trial set population for all hypoglycaemia definitions except for the nocturnal timescale 00.01-07.59. CONCLUSIONS: Compared with insulin glargine, insulin degludec is associated with lower rates of nocturnal hypoglycaemia in people with Type 2 diabetes mellitus, and similar or lower rates in Type 1 diabetes mellitus, across different definitions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".