Decision-Making About Newborn Screening Panels in Canada: Risk Management and Public Participation
Bibliographic record
Abstract
Abstract Newborn Bloodspot Screening (NBS) enables diagnosis and early treatment of rare diseases in non-symptomatic neonates. NBS has well-documented benefits for babies, their families, and the healthcare system at large. In recent decades, rapid advances in screening technologies enabled the proliferation of testable diseases. This has led to increased discussion of both the benefits relevant to decision-making but also the health, economic and ethical challenges associated with the expansion of NBS panels. However, technological capability is not the sole driver of panel expansions, and we suggest that decisions to add a condition to the screening panel constitute exercises in risk management. Using a risk governance lens, this chapter examines procedures that govern decision-making concerning screening panel additions in several Canadian NBS programs. Specifically, we draw on an analysis of documents in the public domain and interviews with individuals associated with Canadian NBS programs to identify the risk management tools that are applied. Our analysis indicates that there is a reliance on the advice of experts and economic controls but limited public participation in decisions about screening panels. We conclude with a discussion of why democratization might strengthen decision-making and offer recommendations to practitioners and scholars regarding next steps and future research.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".