Defining ethical criteria to guide the expanded use of Noninvasive Prenatal Screening (NIPS): Lessons about severity from preimplantation genetic testing
Bibliographic record
Abstract
We hypothesized that ethical criteria that guide the use of preimplantation genetic testing (PGT) could be used to inform policies about expanded use of non-invasive prenatal screening (NIPS). We used a systematic review of reasons approach to assess ethical criteria used to justify using (or not using) PGT for genetic conditions. Out of 1135 identified documents, we retained and analyzed 216 relevant documents. Results show a clear distinction in acceptability of PGT for medical vs. non-medical conditions. Criteria to decide on use of PGT for medical conditions are largely based on their severity, but there is no clear definition of "severity". Instead, characteristics of the condition that relate to severity are used as sub-criteria to assess severity. We found that characteristics that are used as sub-criteria for assessing severity include monogenic etiology, high penetrance, absence of treatment, early age of onset, shortened lifespan, and reduced quality of life. Consensus about the use of PGT is highest for conditions that meet most of these criteria. There is no consensus around the acceptability of using PGT to detect non-medical conditions. We propose that the same severity criteria could be used by policymakers to assess the acceptability of using other genetic tests in screening and practice, including for the use of NIPS for additional conditions as indications broaden.
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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 | 0.173 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".