The Canadian Pharmacogenomics Network for Drug Safety: A Model for Safety Pharmacology
Bibliographic record
Abstract
BACKGROUND: Adverse drug reactions (ADRs) rank as one of the top 10 leading causes of death in the developed world, and the direct medical costs of ADRs exceed $100 billion annually in the United States alone. Pharmacogenomics research seeks to identify genetic factors that are responsible for individual differences in drug efficacy and susceptibility to ADRs. This has led to several genetic tests that are currently being used to provide clinical recommendations. The Canadian Pharmacogenomics Network for Drug Safety is a nation-wide effort established in Canada to identify novel predictive genomic markers of severe ADRs in children and adults. A surveillance network has been established in 17 of Canada's major hospitals to identify patients experiencing specific ADRs and to collect biological samples and relevant clinical history for genetic association studies. To identify ADR-associated genetic markers that could be incorporated into predictive tests that will reduce the occurrence of serious ADRs, high-throughput genomic analyses are conducted with samples from patients that have suffered serious ADRs and matched control patients. SUMMARY: ADRs represent a significant unmet medical problem with significant morbidity and mortality, and Canadian Pharmacogenomics Network for Drug Safety is a nation-wide network in Canada that seeks to identify genetic factors responsible for interindividual differences in susceptibility to serious ADRs. CONCLUSIONS: Active ADR surveillance is necessary to identify and recruit patients who suffer from serious ADRs. National and international collaborations are required to recruit sufficient patients for these studies. Several pharmacogenomics tests are currently in clinical use to provide dosing recommendations, and the number of pharmacogenomics tests is expected to significantly increase in the future.
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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 | 0.032 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".