Genotypic Approaches to Therapy in Children
Bibliographic record
Abstract
A striking failure of modern medicine is the debilitating and lethal consequences of adverse drug reactions (ADRs), which rank as one of the top 10 leading causes of death and illness in the developed world with direct medical costs of 137-177 billion annually US dollars in the USA. Although many factors influence the effect of medications (e.g., age, organ function, drug interactions), genetic factors account for 20% to 95% of drug response variability and play a significant role in the incidence and severity of ADRs. The field of pharmacogenomics seeks to identify genetic factors responsible for individual differences in drug efficacy and ADRs. Pharmacogenomics has led to several genetic tests that provide clinical dosing recommendations. The Genetic Approaches to Therapy in Children (GATC) project is a national project established in Canada to identify novel predictive genomic markers for severe ADRs in children. An ADR surveillance network has been established in eight of Canada's major children's hospitals, serving up to 75% of all Canadian children. The goal of the project is to identify patients experiencing specific ADRs and matched controls, collect DNA samples, and apply genomics-based technologies to identify ADR-associated genetic markers with the goal of preventing serious ADRs in susceptible children.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".