Pattern of semaglutide prescription in a real-world Canadian patient cohort
Bibliographic record
Abstract
AIMS: Despite the growing interest in the broad applications of semaglutide, real-world data on its use in weight-loss is limited. This study aims to explore the pattern of semaglutide prescriptions in a Canadian family medicine practice. METHODS: This retrospective study included patients ≥ 18 years who were enrolled in Sunnybrook Academic Family practice in Toronto, Canada and prescribed semaglutide between January 2018 and April 2024. Baseline demographics, weight measurements up to 16 months, and prescription details were collected. Descriptive statistics was used to illustrate the patterns of semaglutide prescription. RESULTS: . Semaglutide was discontinued due to side effects in 11 (3.3 %) within one month and 27 (8.1 %) at any time. There was an increasing trend in semaglutide prescriptions from 2018 to 2023. There were increasing semaglutide prescriptions for weight-loss, and prescriptions by family physicians compared to specialists. Follow up measurements showed a mean weight-loss of 7.5 % in 212 patients. CONCLUSIONS: In an Ontario academic family practice, semaglutide is being more frequently prescribed in the primary care setting, particularly for weight loss.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".