SAFETY SIGNAL DETECTION OF CARDIAC DISORDERS ADVERSE DRUG EVENTS FOR AZITHROMYCIN IN PEDIATRIC POPULATION USING HEALTH CANADA ADVERSE EVENT REPORTING SYSTEM DATABASE
Bibliographic record
Abstract
Objective: We conducted signal detection of adverse drug events reported in Health Canada adverse event reporting system database “MedEffect” for azithromycin, a macrolide derivative and the first azalide antimicrobial agent to review the cardiac disorders adverse drug events (ADEs) in pediatric population with the drug labels of selected countries including India, USA, UK, Canada, Switzerland, Australia, New Zealand.Methods: We extracted data between January 1965 and June 2016 from the Canada adverse event reporting system database “MedEffect”. Frequentist and Bayesian methods were used to calculate disproportionality distribution of drug-adverse event (AE) pairs. The AE which was detected by all the three indices of proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC) was identified as a potential signal. AE reports for azithromycin, among which 3651 reports were attributed to paediatrics.Results: The signal detected by PRR and ROR for tachycardia associated with azithromycin were found to be 1.3 and for cardiovascular disorder were 1.2. The IC for azithromycin by a Bayesian method was 0.3 for both, tachycardia and cardiovascular disorder. Both AEs of cardiovascular disorder and tachycardia were detected as potential signals of azithromycin for the paediatric population. Comparing drug labels of 7 countries in paediatric population, both adverse events were not listed on any of the labels of seven countries against the pediatric population.Conclusion: We detected 2 new potential signals of azithromycin which were not listed on the labels of 7 countries. Therefore, it should be accompanied by a signal evaluation including causal association, clinical significance, and preventability.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".