Parental Preferences for Expanded Newborn Screening: What Are the Limits?
Bibliographic record
Abstract
The use of next-generation sequencing technologies such as genomic sequencing in newborn screening (NBS) could enable the detection of a broader range of conditions. We explored parental preferences and attitudes towards screening for conditions for which varying types of treatment exist with a cross-sectional survey completed by 100 parents of newborns who received NBS in Ontario, Canada. The survey included four vignettes illustrative of hypothetical screening targets, followed by questions assessing parental attitudes. Chi-square tests were used to compare frequency distributions of preferences. Results show that most parents supported NBS for conditions for which only supportive interventions are available, but to a significantly lesser degree than those with disease-specific treatments (99% vs. 82–87%, p ≤ 0.01). For conditions without an effective treatment, the type of supportive care and age of onset of the condition did not significantly alter parent perceptions of risks and benefits. Parents are interested in expanded NBS for conditions with only supportive interventions in childhood, despite lower levels of perceived benefit for the child and greater anticipated anxiety from screen-positive results. These preferences suggest that the expansion of NBS may require ongoing deliberation of perceived benefits and risks and enhanced approaches to education, consent, and support.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".