381-P: Real-World Evidence that Impaired Awareness of Hypoglycemia Increases Severe Hypoglycemia Rates in T2DM (InHypo-DM Study)
Bibliographic record
Abstract
Impaired awareness of hypoglycemia (IAH) has been linked to an increased rate of severe hypoglycemia (SH) in T1DM. Yet, few investigations have focused on quantifying this relationship in the context of T2DM, particularly from a pragmatic epidemiological lens. This study leverages the value of self-reported SH data to explore the real-world, population-based effect of IAH severity on SH rates in T2DM. A validated questionnaire (InHypo-DMPQ) was administered online to a nationally representative panel comprising Canadians (≥18 years) with T2DM using insulin and/or secretagogues. Data were collected on respondents’ socio-demographic/clinical traits; self-reported incidence of SH (in the past year); and IAH severity, trisected by no, moderate, and severe impairment. Multivariable negative binomial regression (NBR) analysis was used to isolate the effect of IAH on SH. A directed acyclic graph was devised to identify the minimally sufficient adjustment set. Of the 452 complete respondents (mean age: 53.2 (SD: 14.7) years; male: 56%), 6% were classified with severe IAH, 67% with moderate IAH, and 27% with no IAH. Those with severe IAH had the highest crude annual SH rate (5.89 events/person-year, 95% CI: 5.01-6.88), which over doubled and tripled the SH rate in people with moderate IAH (p<0.001) and no IAH (p<0.001), respectively. The adjusted NBR analysis revealed a statistically significant association between IAH and SH (p=0.039). Individuals with severe IAH reported an adjusted annual SH rate that was 3.23 (95% CI: 1.13-9.27, p=0.029) times greater than those with no IAH. A similar trend was observed for moderate IAH versus no IAH (p=0.038). This real-world, population-based study provides timely insight into the high prevalence of moderate and severe IAH in people with T2DM using insulin and/or secretagogues. The marked impact of IAH on increased SH rates underscores a pressing need for the clinical prioritization of IAH assessment and management in T2DM. Disclosure A. Ratzki-Leewing: None. S.B. Harris: Advisory Panel; Self; AstraZeneca, Janssen Pharmaceuticals, Inc., Lilly/Boehringer Ingelheim, Merck & Co., Inc., Novo Nordisk A/S, Sanofi. Consultant; Self; AstraZeneca, Janssen Pharmaceuticals, Inc., Lilly/Boehringer Ingelheim, Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; Abbott, AstraZeneca, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk A/S, Sanofi. Other Relationship; Self; Canadian Diabetes Association, Canadian Institutes of Health Research, The Lawson Foundation. N.H. Au: None. S. Webster-Bogaert: None. J.B. Brown: None. S.M. Reichert: Advisory Panel; Self; Abbott, AstraZeneca, Novo Nordisk Inc., Sanofi, Servier. Research Support; Self; Canadian Institutes of Health Research. Speaker's Bureau; Self; Abbott, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc., Sanofi. B.L. Ryan: None. Funding Sanofi Canada
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".