Underreporting of adverse events to health authorities by healthcare professionals: a red flag-raising descriptive study
Bibliographic record
Abstract
BACKGROUND: An adverse event (AE) is any undesirable medical manifestation in an individual who has received pharmacological treatment. To be considered serious (SAE), it needs to meet minimally one of the severity criteria by Health Canada. The most recent data (2006) suggested that AEs were underreported (<6%) to health authorities. In Canada, since the implementation of Vanessa's Law (2019), hospitals are required to report SAEs; however, this law remains relatively unknown. The objectives of the study were: (i) to document the incidence of any AE and SAE over time in a 'real' clinical context, (ii) to quantify SAEs reported to Health Canada, and (iii) to assess whether Vanessa's Law has led to an increase in mandatory reporting to Health Canada. METHODS: We carried out a descriptive retrospective study at the Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval, including 500 patients hospitalized between 1 January 2018 and 31 December 2021 and randomized into 4 cohorts (125 patients/year). Descriptive and comparative analyses were performed. RESULTS: The characteristics of the cohorts were: 43.6% women; median age: 69 years (min-max: 21-96 years), number of comorbidities: 4 (1-12). During their hospitalization, patients consumed 18 different drug products (2-56) and the median of observed SAEs/patients was 0 (0-10) (total: 302). The overall percentage of SAEs reported to Health Canada was 0%, before and following the implementation of Vanessa's Law. CONCLUSION: According to 500 electronic medical records, SAEs were underreported to Health Canada, even after the implementation of Vanessa's law.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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".