The implementation of an enhanced clinical model to improve the diagnostic yield of exome sequencing for patients with a rare genetic disease: A Canadian experience
Bibliographic record
Abstract
The introduction of clinical exome sequencing (ES) has provided a unique opportunity to decrease the diagnostic odyssey for patients living with a rare genetic disease (RGD). ES has been shown to provide a diagnosis in 29%-57% of patients with a suspected RGD, with as many as 70% remaining undiagnosed. There is a need to advance the clinical model of care by more formally integrating approaches that were previously considered research into an enhanced diagnostic workflow. We developed an Exome Clinic, which set out to evaluate a workflow for improving the diagnostic yield of ES for patients with an undiagnosed RGD. Here, we report the outcomes of 47 families who underwent clinical ES in the first year of the clinic. The diagnostic yield from clinical ES was 40% (19/47). Families who remained undiagnosed after ES had the opportunity for follow-up studies that included phenotyping and candidate variant segregation in relatives, genomic matchmaking, and ES reanalysis. This enhanced diagnostic workflow increased the diagnostic yield to 55% (26/47), predominantly through the resolution of variants and genes of uncertain significance. We advocate that this approach be integrated into mainstream clinical practice and highlight the importance of a coordinated translational approach for patients with RGD.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".