Adverse drug reaction active surveillance: developing a national network in Canada's children's hospitals
Bibliographic record
Abstract
PURPOSE: Adverse drug reactions (ADRs) rank as the fifth leading cause of death in the western world. The nature and scope of these ADRs in children are not predictable based on post market surveillance reports that rely heavily on adult drug experience. The genotype-specific approaches to therapy in childhood (GATC) national ADR network was established to identify specific ADRs and to improve drug safety through identification of predictive genomic biomarkers of drug risk. METHODS: GATC set out to establish a national network of trained surveillance clinicians in pediatric hospitals across Canada. Surveillance clinicians identified, enrolled, and collected clinical data and biological samples from ADR cases and controls. Surveillance was targeted to three ADRs: anthracycline-induced cardiotoxicity, cisplatin-induced hearing impairment, and codeine-induced mortality in breastfed infants. RESULTS: The initial surveillance site was established in September 2005, with 10 sites fully operational by 2008. In 3 years, GATC enrolled 1836 ADR cases and 13188 controls. Target numbers were achieved for anthracycline-induced cardiotoxicity. Modified target numbers were nearly attained for cisplatin-induced hearing impairment. Codeine-induced infant mortality in a breastfed infant was discovered by GATC investigators. A case-control study was subsequently conducted. CONCLUSION: GATC has demonstrated a model of active and targeted surveillance that builds an important step toward the goal of personalized medicine for children. Effective communication, site-specific solutions and long-term sustainability across the network are critical to maintain participation and productivity. GATC may provide a framework of ADR surveillance that can be adapted by other countries and healthcare systems.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".