1444-P: Impact of Semaglutide Shortage in Canadian Adults and Response in a Community Endocrinology Setting (SCARCITY Study)
Bibliographic record
Abstract
Introduction and Objective: Semaglutide once-weekly(SOW)/Ozempic is used for the management of T2DM and/or obesity (off-label) in Canada. There was a supply shortage of SOW during Oct 2023- Jan 2024. We aimed to explore the glycemic impact, management changes and sentiments related to the shortage. Methods: A phone survey of 159 consenting adults prescribed SOW for T2DM and/or obesity Results: Forty-six (28.9%) said they were affected by the shortage and had to either stop, lower the dose or switch drugs. Those affected were more likely to feel that SOW should be equally accessible to both-those with T2DM and obesity; and had higher shortage-related dissatisfaction. HbA1c was not significantly different between those affected and not affected. Conclusion: Canadians affected by SOW shortage did not feel more strongly that it should only be used for those with T2DM. Glycemic control did not significantly worsen due to the ~4months of drug shortage Disclosure A.B. Jain: Advisory Panel; Abbott. Speaker's Bureau; Abbott. Research Support; Abbott. Advisory Panel; Novo Nordisk. Research Support; Novo Nordisk. Speaker's Bureau; Novo Nordisk. Research Support; AstraZeneca, Amgen Inc. Speaker's Bureau; Amgen Inc. Advisory Panel; Amgen Inc. Research Support; Sanofi. Advisory Panel; Bausch Health. Speaker's Bureau; Bausch Health. Advisory Panel; Bayer Pharmaceuticals, Inc. Speaker's Bureau; Bayer Pharmaceuticals, Inc, Boehringer-Ingelheim. Advisory Panel; Dexcom, Inc. Speaker's Bureau; Dexcom, Inc. Advisory Panel; Eli Lilly and Company. Speaker's Bureau; Eli Lilly and Company. Advisory Panel; HLS Therapeutics. Speaker's Bureau; HLS Therapeutics, Medtronic. Advisory Panel; Insulet Corporation. Speaker's Bureau; Insulet Corporation. Advisory Panel; Sandoz. K. Fan: None. H. Rajpar: None. H. Singh: None. L. Beltran: None.
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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 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".