Nasal Glucagon was Efficacious in Reversing Insulin-Induced Hypoglycemia Across a Range of Nadir Blood Glucose Levels
Bibliographic record
Abstract
Background Nasal glucagon (NG) is a ready-to-use rescue treatment for severe hypoglycemia. We evaluated efficacy and safety of NG in reversing insulin-induced hypoglycemia for a range of nadir blood glucose (BG) levels in adults with T1D or T2D. Methods Post-hoc analyses included data from 3 randomized, cross-over studies. BG response, treatment success, and treatment-emergent adverse events (TEAE) were evaluated across nadir (baseline) BG levels for NG and reconstituted injectable glucagon (IG). Treatment success was defined as an increase in BG to ≥ 70 mg/dL or increase of ≥ 20 mg/dL from nadir within 30 min of receiving glucagon. Results A similar proportion of NG (99.5% [213/214]) and IG participants (100% [214/214]) achieved treatment success. Mean times from glucagon administration to BG increase of ≥ 20 mg/dL for nadir BG categories of < 40, ≥ 40 and < 50, ≥ 50 and < 60, and ≥ 60 mg/dL were 14.2, 14.4, 13.2, and 12.3 min for NG, and 13.4, 12.6, 11.3, and 10.6 min for IG, respectively. Mean max BG for NG and IG were 176.2 and 191.3 mg/dL, respectively, over the study period. The association between nadir BG and max BG increase within 30 min was only significant for IG. No significant association between TEAE occurrence and nadir BG for NG or for IG was observed. Conclusion NG was well-tolerated and efficacious in reversing insulin-induced hypoglycemia across various nadir BG levels. Publication History Article published online: 26 May 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".