347-P: Why Some Americans Use Health Care following Severe Hypoglycemia, and Why Some Do Not: Baseline Results of the iNPHORM Study
Bibliographic record
Abstract
Background: Individual and societal determinants can affect the need and propensity for healthcare utilization (HCU) following diabetes-related severe hypoglycemia (SH). This is the first US study to explore the real-world risk factors of HCU- versus non HCU-based SH. Methods: Data were collected online from a generalized cohort of Americans (≥18 years old) with type 1 or type 2 diabetes (T1D, T2D) on insulin and/or secretagogues. Multivariable logistic regression using backward selection was performed to identify the socio-demographic/clinical risk factors of past-year HCU- versus non HCU-based SH (daytime/nocturnal SH resulting in hospital or paramedical services). Results: Results are based on 642 (T1D: 22.7%; female: 46.3%) of 1694 baseline respondents who experienced ≥1 SH events (past year). People with T1D were 40.9 (SD:12.5) years old, while those with T2D were 45.4 (SD: 13.3) years old. Among T2D respondents, 42.5% were on insulin and secretagogues, 31.1% were on insulin alone, and 26.4% on secretagogues alone. Almost half (44.6%) of participants (T1D: 29.9%; T2D: 49.0%) reported ≥1 HCU-based SH events (past year). In the final backward logistic model, the odds of past-year HCU-based SH decreased significantly with female sex, increasing age, decreasing income, and suburban or rural (versus urban) living. Diabetes type did not have an independent effect. However, for individuals on combination insulin-secretagogue therapy, the adjusted odds of HCU-based SH were 2- and 3-times that of those on insulin alone and secretagogues alone, respectively. High A1C (versus <7%) also positively correlated with HCU-based SH; though, this association was marginally insignificant (p=0.065). Conclusion: Our study reveals several factors that can promote or reduce the odds of HCU-based SH. Therapeutic optimizations to mitigate non-essential HCU should prioritize patients on combination insulin-secretagogue therapy and those with poor glycemic control. Disclosure A. Ratzki-leewing: Consultant; Self; Eli Lilly and Company, Novo Nordisk, Other Relationship; Self; Sanofi. J. E. Black: None. B. L. Ryan: None. G. Zou: None. S. B. Harris: Advisory Panel; Self; Abbott Diabetes, Abvance Therapeutics, HLS Therapeutics Inc., Lilly Diabetes, Novo Nordisk A/S, Consultant; Self; Boehringer Ingelheim (Canada) Ltd., mdBriefCase, Other Relationship; Self; American Diabetes Association, AstraZeneca, Novo Nordisk Canada Inc., Sanofi. Funding Sanofi Global
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".