Integrated safety and efficacy analysis of dasiglucagon for the treatment of severe hypoglycaemia in individuals with type 1 diabetes
Bibliographic record
Abstract
AIMS: To perform an integrated analysis of the safety and efficacy of dasiglucagon, a glucagon analogue available in a ready-to-use aqueous formulation, to treat severe hypoglycaemia (SH) in type 1 diabetes (T1D). MATERIALS AND METHODS: An integrated analysis of dasiglucagon safety was conducted on data from two placebo-controlled trials (placebo-controlled pool) and two placebo-controlled and four non-placebo-controlled trials (broad pool) in adults with T1D. An integrated analysis of dasiglucagon efficacy was conducted of pooled data and within demographic subgroups from the two placebo-controlled and two non-placebo-controlled trials in adults with T1D. RESULTS: Dasiglucagon had a similar safety and tolerability profile to that of reconstituted glucagon. In the placebo-controlled datasets, no serious adverse events (AEs), AEs leading to withdrawal from the trial, or deaths were reported. The most common causally related AEs were nausea (56.5%) and vomiting (24.6%). The broad pool safety analysis showed similar results. Dasiglucagon efficacy in time to plasma glucose recovery from insulin-induced SH was similar to that of reconstituted glucagon (median 10.0 and 12.0 minutes, respectively) and superior to placebo (median 40.0 minutes; P < 0.0001). The median recovery time was consistent across all placebo-controlled trial subgroups. CONCLUSIONS: Dasiglucagon was well tolerated and effective as a rapid rescue agent for insulin-induced SH in people with T1D.
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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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".