ETHICS EVALUATION REVEALING DECISION-MAKER MOTIVES: A CASE OF NEONATAL SCREENING
Bibliographic record
Abstract
OBJECTIVES: This paper aims to describe the added value of combining cost-effectiveness and ethical evaluations when the preferences of the decision maker toward cost-effectiveness evaluation outcomes are not known, with the French national neonatal screening of cystic fibrosis (CF) as a case-study. METHODS: A cost-effectiveness analysis comparing four CF neonatal screening strategies, with or without DNA testing, was performed. Ethical positions toward their outcomes were described. In addition, a post-hoc analysis of the ethical issues being considered relevant from the decision-makers' perspective was conducted. RESULTS: Two strategies were found equally cost-effective. Among them, choosing the non-DNA or a DNA-based strategy constrains the decision maker to render a judgement between different ethical issues or disagreements associated with the screening program. CONCLUSIONS: The analysis supports the relevance of combining cost-effectiveness and ethics evaluation in developing health policy, as a way to reveal or clarify the motives associated with health. The choice of the decision maker to favor the DNA-based strategy, which was not originally recommended, creates the opportunity to make explicit the role played by ethical issues in the decision.
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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 | 0.061 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".