Current Utilization Patterns of Glucagon-Like Peptide-1 Receptor Agonists
Bibliographic record
Abstract
The real-world use of glucagon-like peptide-1 (GLP-1) receptor agonists (RAs), which are funded for type 2 diabetes mellitus (T2DM) across public drug plans in Canada, was analyzed to determine their current utilization patterns and estimate their suspected use outside of T2DM. Drug plan expenditures in this drug class have increased significantly in recent months and we wanted to assess the extent of the utilization that was “off reimbursement criteria” (i.e., use outside of T2DM) because these drugs have demonstrated efficacy in other conditions that are not currently publicly funded but have regulatory approval (e.g., weight management). Ozempic (semaglutide injection) is the dominant GLP-1 RA brand (> 99% market share among public PT drug plans) and expenditures on it have accelerated. Expenditures on Ozempic have increased from $13.5 million in 2019 to $227 million in 2021. Increasing use of Ozempic can be partially attributed to non-T2DM claims. The proportion of claimants with suspected use outside of T2DM was 15% in Ontario and ranged from 0% to 8% across the other PT public drug plans; suspected use outside of T2DM is projected to be 1 in 5 claimants in Ontario in 2022. Among non-formulary claims (e.g., federal public plans, private insurance), this proportion ranged from 36% to 74% (data from 2021).
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".