Exploring the feasibility, acceptability and impact of genomic newborn screening for rare diseases in England: A study protocol for the Generation Study - Process and Impact Evaluation (Version 2)
Bibliographic record
Abstract
Abstract The role of genomics in healthcare is expanding rapidly and many countries are exploring the possibility of using genomic sequencing to expand current newborn screening programmes. Offering routine genomic newborn screening (gNBS) would allow newborn screening to include a much broader range of rare conditions, but there are many technical, practical, psychosocial, ethical and economic challenges to be addressed. Genomics England and NHS England have established the Generation Study to deliver gNBS for 100,000 births to explore the benefits, challenges, and practicalities of offering gNBS to parents in England. Here we describe the study protocol for the Generation Study - Process and Impact Evaluation, an independent mixed-methods evaluation of the Generation Study. The evaluation will have oversight from a Study Advisory Group that includes academic, clinical and patient representatives and a Patient and Public Involvement and Engagement (PPIE) Advisory Group that includes members from parent and patient organisations and parents with relevant experiences. The Process and Impact Evaluation will examine whether offering gNBS in routine care is feasible and acceptable and inform our understanding of the clinical utility and cost effectiveness of gNBS in England. Through surveys and interviews conducted at two time points we will explore the attitudes and experiences of parents, professionals and patient organisations. We will also consider the clinical, psychosocial and health economic impacts, both positive and negative. The results will be presented at national and international conferences and submitted for peer review and publication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.196 | 0.179 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.055 | 0.013 |
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".