Policy Recommendations for Carrier Testing and Predictive Testing in Childhood: A Distinction That Makes a Real Difference
Bibliographic record
Abstract
The genetic testing of children raises many ethical concerns. This paper examines how five position statements from Canada, UK and USA, which present guidelines for good practice in this area produce different recommendations for carrier testing and predictive testing. We find that the genetic information generated through carrier testing is routinely presented as less serious than that generated from predictive testing. Additionally, the reproductive implications of predictive testing are also routinely erased. Consequently, the papers argue strongly against predictive testing but advise caution against carrier testing in somewhat weaker terms. We argue that these differences rest on assumptions about the status of reproduction in people's lives and on an ethical stance that foregrounds the self over others. We propose that questioning the crude and sharp distinction between carrier and predictive testing in principle may enable practitioners and parents/families to make more nuanced decisions in practice.
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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 | 0.069 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.038 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 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".