Comparison of Intranasal and Injectable Glucagon Administration Among Pediatric Population Responders
Bibliographic record
Abstract
Aims: Ease of use and acceptability of nasal versus injectable glucagon (IG) among pediatric responders have been little investigated. This study compared the performance of administering nasal and IG in parents of youth with type 1 diabetes (T1D) and in school workers. Enablers and barriers associated with each glucagon and preferred glucagon administration learning modality were also evaluated. Methods: Three months after watching short pedagogical videos, 30 parents and 30 school workers performed simulated scenarios where they administered both glucagon. Completion time and successful execution of critical steps were collected. Interviews assessed preferred learning modalities, barriers, and enablers associated with each glucagon. Results: Both groups administered nasal glucagon faster than IG (median [interquartile range]: parents 19 [12–29] vs. 97 [71–117] s, P < 0.001; school workers 24 [16–33] vs. 129 [105–165] s, P < 0.001). A lower proportion of participants successfully executed all critical steps for injectable versus nasal glucagon (significant difference for school workers [53% vs. 90%; P = 0.007] but not for parents [68% vs. 83%; P = 0.227]). Nasal glucagon was preferred for ease of use and acceptability. Preferred learning modalities were a combination of videos and workshops, but videos alone could suffice for nasal glucagon. Conclusions: Nasal glucagon is faster to use, more likely to be successfully administered, and more acceptable than IG for parents of children with T1D and school workers. Nasal glucagon training with videos could improve school workers' involvement in severe hypoglycemia management. Clinical Trial number, URL to the registration: NCT05395000, https://clinicaltrials.gov/ct2/show/NCT05395000
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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 | 0.006 |
| 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 | 0.000 |
| 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".