Feasibility, acceptability and clinical outcomes of the BabyScreen+ genomic newborn screening study
Bibliographic record
Abstract
Incorporating genomic sequencing into newborn screening will dramatically increase the number of detectable conditions but evidence is needed to guide policy. The prospective BabyScreen+ cohort study screened 1,000 newborns from the state of Victoria, Australia for variants in 605 genes associated with early-onset, severe, treatable conditions using whole-genome sequencing performed on dried blood spot cards. Sixteen infants (1.6%) were identified as having high-chance results. Of these, only one was detected by standard newborn screening. Average time to genomic newborn result was 13 days. Clinical impact ranged from instituting preventative measures or surveillance to active management, including transplantation. Twenty relatives received a diagnosis following cascade testing. Median parental decisional regret was low (median 0, interquartile range 0-10); >99% of participants thought genomic newborn screening should be available to all parents. Our study demonstrates the feasibility of clinically accredited genomic newborn screening, using a scalable model that is highly acceptable to parents. Future research is needed to address issues of scalability and equity.
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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 | 0.070 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".