CRASH: CONVERSATIONS AND REACTIONS AROUND SEVERE HYPOGLYCEMIA: A GLOBAL STUDY
Bibliographic record
Abstract
Background and Aims: There is little known about the experience of persons with diabetes (PWD) and their caregiversu2019 (CG) knowledge of a severe hypoglycemic event (SHE), and the use of glucagon to treat such an event. The CRASH (Conversations and Reactions Around Severe Hypoglycemia) cross-sectional survey was developed to address this research gap.Method: Medical research panels were used to identify and recruit 400 participants (200 PWD and 200 CG, evenly divided between type 1 and type 2 diabetes) in each of six countries: Canada, Germany, China, Spain, UK, and US. All participants were age u226518 years old. Inclusion criteria for PWD included self-report of insulin therapy and having had experienced a SHE within the past 3 years. CG inclusion required self-report of caring for a PWD u22654 years old on insulin therapy, who had experienced a SHE within the past 3 years. Participants completed a 30-minute online survey that examined their understanding of a SHE and its treatment, their actions during the SHE, and finally, what precautionary actions occurred after the SHE in terms of prevention and emergency preparedness. Descriptive analyses were conducted. Management and impact of a SHE were analyzed by subgroups including: diabetes type, participant type, age, glucagon use. Results: Data from 400 UK participants were analyzed. Findings describe symptom recognition, knowledge and use of self-management strategies and professional emergency medical assistance. Conclusion: Results provide much needed insights of the impact SHE (both personal and societal perspectives) and current glucagon treatment have on PWD and their CG.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".