Pharmacogenomics in Pediatric Oncology: Mitigating Adverse Drug Reactions While Preserving Efficacy
Bibliographic record
Abstract
Cancer is the leading cause of death in American children older than 1 year of age. Major developments in drugs such as thiopurines and optimization in clinical trial protocols for treating cancer in children have led to a remarkable improvement in survival, from approximately 30% in the 1960s to more than 80% today. Short-term and long-term adverse effects of chemotherapy still affect most survivors of childhood cancer. Pharmacogenetics plays a major role in predicting the safety of cancer chemotherapy and, in the future, its effectiveness. Treatment failure in childhood cancer-due to either serious adverse effects that limit therapy or the failure of conventional dosing to induce remission-warrants development of new strategies for treatment. Here, we summarize the current knowledge of the pharmacogenomics of cancer drug treatment in children and of statistically and clinically relevant drug-gene associations and the mechanistic understandings that underscore their therapeutic value in the treatment of childhood cancer.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".