Comparison of the Completeness of Spontaneously Reported Adverse Drug Reactions by Consumers, Healthcare Professionals, and Pharmaceutical Companies: An Evaluation of Databases From Two High‐Income Countries
Bibliographic record
Abstract
This study assessed whether the completeness of spontaneously reported adverse drug reaction (ADR) reports differs between consumers and healthcare professionals when submitted directly to regulators, and how this compares to reports from pharmaceutical companies. ADR reports (2014-2023) were obtained from public databases in Canada and the United Kingdom (UK), focusing on the medicine classes sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 receptor agonists, and dipeptidyl peptidase-4 inhibitors. ADR report completeness was assessed using vigiGrade tool variables. Descriptive statistics and chi-square tests were used for analysis. A total of 17 897 reports were analyzed-13 613 from the UK Yellow Card Scheme and 4284 from Canada. Most Canadian reports were submitted by pharmaceutical companies (55%), while in the UK, healthcare professionals submitted the majority (69%). Few reports were submitted directly by consumers in either Canada (4%) or the UK (7%). In Canada, the average completeness was 82% for consumer and healthcare professional reports and 57% for pharmaceutical companies. In the UK, completeness was 80% (consumers), 82% (healthcare professionals), and 69% (pharmaceutical companies). Canadian pharmaceutical company reports were significantly less complete for age, sex, outcome, dose, indication, and route of administration (all p < 0.001). In the UK, they were less complete for age, sex, and route of administration (all p < 0.001). In conclusion, reports submitted directly to regulators by consumers and healthcare professionals were more complete than those from pharmaceutical companies. The low consumer reporting rate, yet high completeness rate, highlights the need to encourage direct reporting to regulators to improve medicine safety monitoring.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".