Rapid Genetic Testing in Pediatric and Neonatal Critical Care: A Scoping Review of Emerging Ethical Issues
Bibliographic record
Abstract
BACKGROUND: Rapid genome-wide sequencing (rGWS) is being increasingly used to aid in prognostication and decision-making for critically ill newborns and children. Although its feasibility in this fast-paced setting has been described, this new paradigm of inpatient genetic care raises new ethical challenges. OBJECTIVE: A scoping review was performed to (1) identify salient ethical issues in this area of practice; and (2) bring attention to gaps and ethical tensions that warrant more deliberate exploration. METHODS: Data sources, Ovid Medline and Cochrane Central Register of Controlled Trials, were searched up to November 2021. Articles included were those in English relating to rGWS deployed rapidly in a critical care setting. Publications were examined for ethical themes and were further characterized as including a superficial or in-depth discussion of that theme. New themes were inductively identified as they emerged. RESULTS: Ninety-nine studies, published in 2012 or thereafter, met inclusion criteria. Themes identified elaborated upon established ethical principles related to beneficence and nonmaleficence (ie, clinical utility, medical uncertainty, impact on family, and data security) autonomy (ie, informed consent), and justice (ie, resource allocation and disability rights). Many themes were only narrowly discussed. CONCLUSIONS: The application of rGWS in neonatal and pediatric acute care is inherently tied to ethically charged issues, some of which are reported here. Attention to the ethical costs and benefits of rGWS is not always discussed, with important gaps and unanswered questions that call for ongoing focus on these ethical considerations in this next application of acute care genomics.
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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 | 0.057 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".