Interest in Newborn Genetic Testing: A Survey of Prospective Parents and the General Public
Bibliographic record
Abstract
PURPOSE: Newborn screening (NBS) panels continue to expand, yet there are too few data on public attitudes toward testing in the newborn period to indicate whether there is support for such testing. We measured interest in newborn testing for several autosomal recessive disorders and reasons for interest. METHODS: A cross-sectional, pen and paper survey was administered to the general public and prospective parents attending prenatal classes in Eastern Canada between April and December, 2010. RESULTS: A total of 648 individuals completed surveys. Interest in newborn testing for inherited hearing loss, vision loss, and neurological disorders was high (over 80% would have their newborn tested). The attitudes of prospective parents and students were positive, but somewhat less so than members of the general public. Across all disorders, interest in testing was driven by the desire to be prepared for the birth of a child with a genetic disorder. Significantly more people would use the information from testing for fatal neurological disorders in future reproductive decisions than the information generated by newborn testing for inherited hearing or vision loss. CONCLUSION: Interest is high in newborn testing for a variety of conditions, including those for which no effective treatment exists. Findings lend support to the expansion of NBS panels to include those disorders currently lacking treatment and highlight the value of including the views of diverse stakeholders, including prospective parents, in screening policies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".