Glucagon-like Peptide-1 Receptor Agonist Use in Hospital: A Multicentre Observational Study
Bibliographic record
Abstract
BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are effective medications for type 2 diabetes mellitus (T2DM) and obesity, yet their uptake among individuals most likely to benefit has been slow. METHODS: We conducted a cross-sectional analysis of medication exposure in adults hospitalized at 16 hospitals in Ontario, Canada, between 2015 and 2022. We estimated the proportions of those with T2DM, obesity, and cardiovascular disease. We identified the frequency of GLP-1RA use and conducted multivariable logistic regression to identify factors associated with their use. RESULTS: Across 1,278,863 hospitalizations, 396,084 (31%) patients had T2DM and approximately 327,844 (26%) had obesity. GLP-1RA use (n=1,274) was low among those with a diagnosis of T2DM (0.3%) or obesity (0.7%), despite a high prevalence of cardiovascular disease (36%). In contrast, the use of diabetes medications lacking cardiovascular benefit was high during inpatient hospitalizations related to diabetes: 60% (n=236,612) received insulin and 14% (n=54,885) received a sulfonylurea. Apart from T2DM (odds ratio [OR]=29.6, 95% confidence interval [CI] 23.5 to 37.2), characteristics associated with greater odds of receiving a GLP-1RA were seen in those 50 to 70 years of age (OR=1.71, 95% CI 1.38 to 2.11) compared with those <50 years of age, glycated hemoglobin >9% (OR=1.83, 95% CI 1.36 to 2.47) compared with <6.5%, and highest income quintile (OR=1.73, 95% CI 1.45 to 2.07) compared with lowest income quintile. CONCLUSION: Knowledge translation interventions are needed to address the low adoption of GLP-1RAs among hospitalized patients with T2DM and obesity, who are the most likely to benefit from this treatment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".