Cascade health service use in family members following genetic testing in children: a scoping literature review
Bibliographic record
Abstract
Cascade genetic testing is the identification of individuals at risk for a hereditary condition by genetic testing in relatives of people known to possess particular genetic variants. Cascade testing has health system implications, however cascade costs and health effects are not considered in health technology assessments (HTAs) that focus on costs and health consequences in individual patients. Cascade health service use must be better understood to be incorporated in HTA of emerging genetic tests for children. The purpose of this review was to characterise published research related to patterns and costs of cascade health service use by relatives of children with any condition diagnosed through genetic testing. To this end, a scoping literature review was conducted. Citation databases were searched for English-language papers reporting uptake, costs, downstream health service use, or cost-effectiveness of cascade investigations of relatives of children who receive a genetic diagnosis. Included publications were critically appraised, and findings were synthesised. Twenty publications were included. Sixteen had a paediatric proband population; four had a combined paediatric and adult proband population. Uptake of cascade testing varied across diseases, from 37% for cystic fibrosis, 39% to 65% for hypertrophic cardiomyopathy, and 90% for rare monogenic conditions. Two studies evaluated costs. It was concluded that cascade testing in the child-to-parent direction has been reported in a variety of diseases, and that understanding the scope of cascade testing will aid in the design and conduct of HTA of emerging genetic technologies to better inform funding and policy decisions.
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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 | 0.014 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".