Genomic sequencing in newborn screening: balancing consent with the right of the asymptomatic at-risk child to be found
Bibliographic record
Abstract
In this paper, we explore key aspects of the complex ethical and legal landscape surrounding consent in the context of incorporating genomic sequencing into existing newborn bloodspot screening programs. In particular, we consider the potential impact of genomic sequencing on the health rights of the child in relation to existing consent practices in newborn screening. We begin with an introduction to newborn screening programs and their population health goals. We then discuss public health ethics as a rationale underpinning newborn screening before turning to consent. We go on to describe seven current research projects on genomic sequencing in newborn screening and then introduce the 'right of the asymptomatic at-risk child to be found' as a useful concept to draw on when considering consent to newborn screening. We draw on this novel right to argue for the adoption of "appropriate consent" when it comes to certain uses of genomics in newborn screening. We contend that, for 'virtual panels' at least, appropriate consent proportionately balances the ongoing universality of newborn screening for important health conditions with an acknowledgement of the complex outcomes that bringing a complicated diagnostic technology into the screening domain will generate.
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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 | 0.110 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".