Toward the diagnosis of rare childhood genetic diseases: what do parents value most?
Bibliographic record
Abstract
Genomic testing is becoming routine for diagnosing rare childhood genetic disease. Evidence underlying sustainable implementation is limited, focusing on short-term endpoints such as diagnostic yield, unable to fully characterize patient and family valued outcomes. Although genomic testing is becoming widely available, evidentiary and outcomes uncertainty persist as key challenges for implementation. We examine whether the current evidence base reflects public tolerance for uncertainty for genomics to diagnose rare childhood genetic disease. We conducted focus groups with general population parents in Vancouver, Canada, and Oxford, United Kingdom, to discuss expectations and concerns related to genomic testing to diagnose rare childhood genetic disease. Applying a purposive sampling technique, recruitment continued until thematic saturation was reached. Transcripts were analysed using thematic analysis. Thirty-three parents participated across four focus groups. Participants valued causal diagnoses alongside management strategies to improve patient health and wellbeing. Further, participants valued expanding the evidence base to reduce evidentiary uncertainty while ensuring security of information. Willingness to pay out of pocket for testing reflected perceived familial health benefit. Diagnostic yield fails to fully capture valued outcomes, and efforts to resolve uncertainty better reflect public priorities. Evaluations of genomic testing that fully integrate valued endpoints are necessary to ensure consistency with best practices and public willingness to accept the uncertain familial benefit.
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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 | 0.020 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".