Offering complex genomic screening in acute pediatric settings: Family decision-making and outcomes
Bibliographic record
Abstract
PURPOSE: Families of children in pediatric acute care who are offered ultrarapid genomic sequencing are making complex decisions during a high-stress period. To reduce complexity for families and clinicians, we offered genomic screening for the child and parents after the completion of diagnostic testing. We evaluated uptake, understanding, and service delivery preferences. METHODS: A cohort of 235 families who had completed ultrarapid diagnostic genomic sequencing at 17 Australian hospitals were offered up to 3 screens on their genomic data: pediatric-onset, adult-onset, and expanded couple carrier screening. We investigated decision making, understanding, and service delivery preferences using surveys at 3 time points (pre counseling, post counseling, and post result) and performed inductive content analysis of pretest genetic counseling transcripts. RESULTS: A total of 119 families (51%) attended genetic counseling with 115 (49%) accepting genomic screening. Survey respondents were more likely to find decisions about couple carrier screening easy (87%) compared with adult (68%; P = .002) or pediatric (71%; P = .01) screening decisions. All respondents with newly detected pathogenic variants accurately recalled this 1 month later. A delayed offer of screening was acceptable to most respondents (78%). CONCLUSION: Separating genomic screening from the stressful diagnostic period is supported by families who demonstrate good knowledge and recall. Our results suggest delaying genomic screening should be trialed more widely.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".