Should secondary pharmacogenomic variants be actively screened and reported when diagnostic genome-wide sequencing is performed in a child?
Bibliographic record
Abstract
This white paper was prepared by the Global Alliance for Genomics and Health Regulatory and Ethics Work Stream's Pediatric Task Team to review and provide perspective with respect to ethical, legal, and social issues regarding the return of secondary pharmacogenomic variants in children who have a serious disease or developmental disorder and are undergoing exome or genome sequencing to identify a genetic cause of their condition. We discuss actively searching for and reporting pharmacogenetic/genomic variants in pediatric patients, different methods of returning secondary pharmacogenomic findings to the patient/parents and/or treating clinicians, maintaining these data in the patient's health record over time, decision supports to assist using pharmacogenetic results in future treatment decisions, and sharing information in public databases to improve the clinical interpretation of pharmacogenetic variants identified in other children. We conclude by presenting a series of points to consider for clinicians and policymakers regarding whether, and under what circumstances, routine screening and return of pharmacogenomic variants unrelated to the indications for testing is appropriate in children who are undergoing genome-wide sequencing to assist in the diagnosis of a suspected genetic disease.
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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 | 0.027 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.009 |
| 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".