Beyond severity: utility as a criterion for setting the scope of RGCS
Bibliographic record
Abstract
Reproductive genetic carrier screening (RGCS) allows prospective parents to identify and act upon their chances of having a child with a genetic condition. In deciding which genetic conditions to include in RGCS, severity is often used as a criterion. However, the concept is inherently complex, subjective and multidimensional, and determinations of severity will remain intractably contested. We propose the concept of utility as a criterion for setting the scope of RGCS, and put forward two central arguments for doing so. First, utility is a more appropriate and effective concept as it responds to context and makes an explicit connection between the purpose of RGCS and the value of information obtained for that purpose: namely, to facilitate reproductive decision-making. Utility comprises both clinical and personal utility, and varies according to the availability and accessibility of reproductive options, including pre-implantation genetic testing, prenatal genetic diagnosis, and termination of pregnancy. Second, there are ethical reasons for preferring utility over severity. Utility is a property of the information gleaned from RGCS, while severity is a property of a genetic condition or of an instance of this condition in a person. While consideration of the severity of genetic conditions is not lost when focusing on utility, the need to rely on value judgements regarding the quality of life of people who live with genetic conditions is circumvented. Therefore, utility should replace severity as justification for the inclusion of genetic conditions in RGCS programmes.
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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 | 0.035 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".