2198-PUB: Real-World Risk Indicators of Impaired Awareness of Hypoglycemia in T2DM (InHypo-DM Study)
Bibliographic record
Abstract
Further investigation into the real-world etiology of impaired awareness of hypoglycemia (IAH) in T2DM is needed. The present study aims to address this research gap by providing pragmatic, population-based insight into IAH and its risk indicators. A validated questionnaire (InHypo-DMPQ) was administered online to a nationally representative panel comprising Canadians (≥18 years) with T2DM taking insulin and/or secretagogues. Data were collected on respondents’ socio-demographic/clinical traits, self-reported hypoglycemia, and severity of IAH (no, moderate, and severe). Multivariable partial proportional odds (PPO) regression was used to identify the relevant risk indicators of moderate and severe IAH. Covariates were selected in consultation with the literature and clinical experts. A complete case analysis of 432 respondents (mean age: 53.0 (SD: 14.7) years; male: 56%) was undertaken. Of these individuals, 28% were classified with severe IAH, 66% with moderate IAH, and 6% with no IAH. Age, diabetes duration, A1C, total annual history of hypoglycemia (any type), therapeutic regimen, comorbidity status, gender, income, education, and drug coverage status were assessed for their influence on IAH severity. Based on the multivariable PPO model, a 5-year increase in diabetes duration decreased the adjusted odds of moderate or severe IAH, relative to no IAH, by 24% (95% CI: 12-35%, p<0.001). Moreover, the adjusted odds of severe IAH classification, versus no or moderate IAH, increased by a factor of 1.09 (95% CI: 1.02-1.15, p=0.008) for every 15 hypoglycemic events reported (in the past year). These results will help inform the improved management of IAH in real-world clinical settings. To reduce the risk of severe IAH in T2DM, clinical interventions should prioritize the reduction of hypoglycemic events. In particular, supporting patients with more recent T2DM diagnoses toward accurately recognizing their hypoglycemia symptoms may forestall the onset of IAH and related sequelae. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".