Identifying predictors of sodium-glucose cotransporter 2 inhibitor and glucagon-like peptide 1 receptor agonist use in hospital among adults with diabetes
Bibliographic record
Abstract
AIMS: To identify factors associated with use of novel diabetes medications among patients hospitalized under general internal medicine. METHODS: We conducted a cohort study of patients with type 2 diabetes mellitus (T2DM) hospitalized in Ontario, Canada between 2015 and 2020. We evaluated the patient- and physician-level factors associated with sodium-glucose cotransporter 2 inhibitor (SGLT2) and glucagon-like peptide 1 receptor agonist (GLP1R) use using a multivariable logistic regression model. RESULTS: There were 253,152 hospitalizations and 68,126 involved patients who had T2DM. Prior to discharge, 3.7 % (N = 2490) of patients with T2DM received an SGLT2 and 0.2 % (N = 121) received a GLP1R. The strongest predictors for receiving a novel diabetes medication were hemoglobin A1C > 9.0 % (Odds Ratio (OR) = 1.81, 95 % Confidence Interval (CI) 1.28, 2.60) and patients aged 40-60 compared with patients <40 years old (OR = 1.81, 95 % CI 1.33, 2.68). The strongest predictors for not receiving a novel diabetes medication were dementia (OR = 0.47, 95 % CI 0.39, 0.56) and creatinine ≥200 μmol/L (OR = 0.11, 95 % CI 0.08, 0.15). Overall, 46.8 % of patients hospitalized with T2DM not receiving a novel diabetes medication would potentially benefit from an SGLT2 inhibitor. CONCLUSIONS: Novel diabetes medications were rarely continued or initiated during hospitalization despite a high prevalence of cardiovascular disease, raising the concern for systematic under-utilization after discharge.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".