Blazing the Trail of Non‐Invasive Prenatal Screening Expanded Use: Healthcare Providers' Perspectives
Bibliographic record
Abstract
OBJECTIVE: Advancements in non-invasive prenatal screening (NIPS) could significantly alter prenatal screening by expanding the range of genetic conditions screened. This study aims to explore the perspectives of healthcare professionals (HCP) on the expanded use of NIPS and explore specifications for the inclusion of genetic conditions. METHOD: Semi-structured interviews were conducted with Canadian HCPs who counsel pregnant individuals about NIPS. The findings were organized around the four ethical pillars of Kater-Kuiper's framework: proportionality (benefits and harms), aim of screening, justice, and societal aspects. RESULTS: Participants chose to assess the proportionality of NIPS using general terms to describe the additional conditions rather than discussing specific conditions to add. They emphasized the importance of clinical validity as crucial for expanding NIPS and ensuring its utility. Participants believed that the aim of NIPS is to enhance reproductive autonomy, and therefore that screening for late-onset conditions could create ethical tensions between parents and the prospective children. Participants also worried that expanded use of NIPS could impact the quality of counselling provided by HCPs and affect autonomy. Justice considerations include the allocation of resources in prenatal care instead of other areas of healthcare. Societal aspects highlighted the different definitions HCPs used to describe 'life-limiting conditions' that could severely affect the future child's health. CONCLUSION: With expanded use of NIPS, clinical validity will vary for each screened condition. Specificity for each condition will influence the quality of consent. HCP estimates that clinical validity, clinical utility, availability of counselling, availability of resources, and societal impacts should be considered when adding genetic conditions to NIPS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".