A population‐based analysis of antidiabetic medications in four Canadian provinces: Secular trends and prescribing patterns
Bibliographic record
Abstract
PURPOSE: To use the Canadian Network for Observational Drug Effect Studies (CNODES) to describe drug utilization of antidiabetic medications in four Canadian provinces. METHODS: With the use of data from CNODES, we constructed cohorts of patients with type 2 diabetes in four Canadian provinces (Manitoba, Ontario, Quebec, and Saskatchewan) who received their first-ever prescription for a noninsulin antidiabetic medication during the study period, defined as the earliest date of data availability in each province (range: 1993-1998) to the latest date of the data extraction in each province (range: 2013-2014). Prescriptions rates were calculated for all prescriptions by class and described over time. RESULTS: Across provinces, we identified 650 830 patients who initiated antidiabetic medications during the study period. In most provinces, the overall prescription rate of antidiabetic medications increased during the last two decades. Metformin particularly increased in popularity, surpassing sulfonylureas in all provinces as the most widely prescribed antidiabetic medication by the early 2000s. Thiazolidinediones grew in popularity from the onset of their availability until 2006 to 2007, at which point they rapidly declined. Dipeptidyl peptidase-4 inhibitors saw substantial growth in several provinces following their addition to provincial formularies in 2008 to 2012, while glucagon-like peptide-1 agonists experienced modest growth. Insulin prescription rates remained constant or steadily increased over the last two decades. CONCLUSIONS: CNODES can be used for cross-jurisdictional drug utilization studies. In Canada, trends in antidiabetic medication prescriptions followed changing guidelines reflecting up-to-date knowledge of drug effectiveness and safety.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".