Implementing genomic newborn screening as an effective public health intervention: sidestepping the hype and criticism
Bibliographic record
Abstract
The development and implementation of population-based newborn screening was one of the most successful public health interventions of the twentieth century. Newborn screening is now routinely provided to almost all infants in many jurisdictions and usually includes a number of Mendelian diseases as well as some other treatable conditions of infancy. Genome-wide sequencing of the DNA that can be obtained from a small drop of an infant’s blood would permit the identification of genomic variants that are predictive of thousands of additional genetic diseases and provide the opportunity to treat many more healthy-appearing babies with childhood-onset disorders. Newborn genomic sequencing could also give parents information about genetic variants associated with conditions for which there is currently no treatment, that do not have onset until much later in life, or that only raise concern in relatives of the infant. However, our knowledge of the penetrance, natural history, and variability of most rare genetic diseases is limited, the clinical validity and utility of genomic diagnosis for many of these conditions have not yet been established, and the value of presymptomatic treatment is often unclear. As a consequence, much of the information obtained through newborn genomic screening may be of no benefit to, or could even harm, a baby. Genomic sequencing data might be stored indefinitely in an infant’s electronic health record, a prospect that raises serious ethical, legal, privacy, and social concerns. Implementing universal genomic newborn screening in accordance with widely-accepted public health disease screening criteria would sidestep most the concerns that have been raised.
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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 | 0.209 | 0.343 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.045 | 0.080 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".