Real‐world incidence and risk factors of level 3 (severe) hypoglycaemia in adults with type 1 or 2 diabetes ( <scp>iNPHORM</scp> , United States)
Bibliographic record
Abstract
AIMS: Level 3 (severe) hypoglycaemia is a serious, yet preventable, complication of insulin- or secretagogue-treated diabetes. However, real-world insight into its incidence and risk factors remains limited. We analysed data from the iNPHORM study to address this gap. MATERIALS AND METHODS: iNPHORM is a prospective internet panel survey of US adults (aged 18-90) with type 1 diabetes (T1D) or insulin- and/or secretagogue-treated type 2 diabetes (T2D). A screener, baseline and 12-monthly follow-up questionnaires captured data on Level 3 hypoglycaemia and participant characteristics. Crude incidence proportions and annualized event rates were calculated. Repeated LASSO selection and negative binomial regression identified risk factors, with separate models for T1D and T2D. RESULTS: Among 985 participants (T1D: 16.7%), 35.0% experienced ≥1 Level 3 event over follow-up (T1D: 45.0%; T2D: 33.0%). The annualized rate was 4.98 EPPY (T1D: 3.56; T2D: 5.26). In T1D, rates were higher among individuals identifying as non-White (RR [rate ratio]: 2.03), with prior diabetes ketoacidosis (RR: 6.49), and greater fear of hypoglycaemia (RR: 1.41). Prior diabetes education (RR: 0.42) was protective. In T2D, rates were higher with younger age (RR: 0.77 per 10-year increase), greater fear of hypoglycaemia (RR: 1.18), more diabetes complications, higher secretagogue dose, more diabetes visits (RR: 1.72), longer use of continuous/flash glucose monitoring, impaired awareness of hypoglycaemia and a greater number of past healthcare-requiring severe hypoglycaemia (RR: 1.12). CONCLUSIONS: Level 3 hypoglycaemia remains common in T1D and T2D. Findings support risk-targeted prevention and diabetes care strategies that co-prioritize safety and effectiveness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".