Analyses of Adverse Drug Reactions–Nationwide Active Surveillance Network: Canadian Pharmacogenomics Network for Drug Safety Database
Bibliographic record
Abstract
Adverse drug reactions (ADRs) are a major problem in modern medicine, representing up to the fourth-highest cause of mortality. Pharmacogenomic tests are 1 of the most promising methods to tackle the challenge of ADRs. The objective of this study was to analyze the clinical and demographic information of the pan-Canadian active surveillance network, Canadian Pharmacogenomics Network for Drug Safety (CPNDS). Information entered into the database by trained active surveillors between May 15, 2005 and May 9, 2017 was collected and analyzed. Specific data included for analysis were number of ADR reports, reports of drug use without ADRs, date of onset of ADR, suspected drugs, concomitant drugs, and fatal ADR cases. The CPNDS database consisted of 93,974 reports of medication use, including 10,475 reports of ADRs, of which 72.6% occurred in pediatric patients (≤21 years old). Self-reported ancestries were predominantly Europe (38.2%), Canada (9.6%), and East Asia (4.9%). The 5 most frequent ADRs were cutaneous ADRs, peripheral neuropathy, cardiotoxicity, central nervous system toxicity, and ototoxicity. The 5 drugs most commonly suspected to cause ADRs were methotrexate, vincristine, doxorubicin, cisplatin, and L-asparaginase. The CPNDS database is a valuable resource to identify clinical and genomic predictors of ADRs. The database also highlights our candidate ADRs for pharmacogenomic discovery research to identify additional ADR biomarkers. Additionally, the database provides information that can be used for developing strategies to prevent ADRs and raises awareness of ADRs among Canadian healthcare professionals.
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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 | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".