Young adults with type 2 diabetes experience high rates of Level 3 Hypoglycemia: A subgroup analysis of the Real-World iNPHORM cohort
Bibliographic record
Abstract
AIMS: This study evaluated the incidence proportion and rate of self-reported Level 3 hypoglycemia and explored associated risk factors in young adults with type 2 diabetes (T2D). METHODS: Subgroup analyses with a one-year U.S.-wide T2D dataset were performed. Retrospective and prospective data on Level 3 hypoglycemia and participant characteristics were analyzed for 207 young adults (18-39 years old) and 436 middle-aged adults (40-64 years old). Age group-stratified multivariable negative binomial regression was used to identify factors associated with Level 3 hypoglycemia. RESULTS: Young adults exhibited a threefold higher incidence of Level 3 hypoglycemia events requiring medical assistance compared to the middle-aged cohort at baseline (p < 0.001). During follow-up, the young adults experienced a twofold higher incidence proportion of Level 3 hypoglycemia (p < 0.001), and n a fivefold higher annualized rate (p < 0.001); they also reported greater hypoglycemia fear (p < 0.001). Distinct sociodemographics, general health and lifestyle factors, diabetes medical history, and diabetes therapy and technology use characteristics were observed in the young-adult group, as were unique risk factors for Level 3 hypoglycemia frequency CONCLUSION: Our results suggest that young adults with T2D are at particularly high risk of Level 3 hypoglycemia, with attributes that differ from those of the traditional middle-aged cohort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".