Impact of Vanessa's Law on the Reporting of Serious Adverse Events: A Retrospective Study Among Antiplatelet Users in a Tertiary-Care Cardiology Centre
Bibliographic record
Abstract
Background: Antiplatelet drugs, such as clopidogrel, ticagrelor, prasugrel, and acetylsalicylic acid, may be associated with a risk of adverse events (AEs). Vanessa's Law was enacted to strengthen regulations to protect Canadians from drug-related side effects (with mandatory reporting of serious adverse events [SAEs]). Objective: To determine whether Vanessa's Law has led to an increase in SAE reporting among antiplatelet users. Methods: This descriptive retrospective study was conducted from January, 2018-December, 2021. Included are 260 adult antiplatelet users (cohorts: 2018 [n = 64]; 2019 [n = 79]; 2020 [n = 73]; 2021 [n = 44]) hospitalized at the Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval. The main diagnostic of hospitalization was coded using the International Classification of Diseases,10th revision, Canadian version, and data related to demographic characteristics, hospitalization length-of-stay, drugs administered, and AEs were extracted. Results: The 260 antiplatelet users were hospitalized mainly for diseases of the circulatory system (codes [I00-I99]; 2018, 75 %; 2019, 71 %; 2020, 71 %; 2021, 77 %) or diseases of the respiratory system (codes [J00-J99]; 2018, 6 %; 2019, 8 %; 2020, 4 %; 2021, 7 %). The median age was 70 years. The median duration of hospital stay was 3 days. Among the 1395 AEs recorded during the study, 12 % were SAEs. None of the SAEs (or AEs) was reported to Health Canada, either before or after Vanessa's Law implementation. Conclusions: These results provide the first picture of reporting trends for SAEs among antiplatelet users in Canada. Investigation of the underreporting of SAEs is needed, as the implementation of a mandatory policy does not seem to have had a favourable impact. Clinical Trial Registration: 135263.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".