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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".