The Canadian Pharmacogenomics Network for Drug Safety: A Model for Safety Pharmacology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it