Guiding Policy Decisions for Genetic Screening: Developing a Systematic and Transparent Approach
Bibliographic record
Abstract
With the ever-widening gap between what is technologically possible and services available, jurisdictions around the world are faced with complex decisions regarding the introduction and expansion of genetic screening programs. A series of literature reviews and consultations with stakeholders and experts led to the development of a decision support guide for genetic screening policy-making. This involved establishing a preliminary list of core criteria synthesized from the growing literature on genetic screening, which was then transformed through a series of consultations into a more elaborate decision guide. Although certain perennial challenges in genetic screening policy-making remain, the decision support guide aims to promote a fair and evidence-informed process that makes explicit the ethical dilemmas often inherent to such policy decisions.
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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 | 0.593 | 0.491 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.042 | 0.030 |
| Open science | 0.017 | 0.037 |
| Research integrity | 0.034 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".