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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".