Utility of whole‐exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care
Bibliographic record
Abstract
An accurate diagnosis is an integral component of patient care for children with rare genetic disease. Recent advances in sequencing, in particular whole-exome sequencing (WES), are identifying the genetic basis of disease for 25-40% of patients. The diagnostic rate is probably influenced by when in the diagnostic process WES is used. The Finding Of Rare Disease GEnes (FORGE) Canada project was a nation-wide effort to identify mutations for childhood-onset disorders using WES. Most children enrolled in the FORGE project were toward the end of the diagnostic odyssey. The two primary outcomes of FORGE were novel gene discovery and the identification of mutations in genes known to cause disease. In the latter instance, WES identified mutations in known disease genes for 105 of 362 families studied (29%), thereby informing the impact of WES in the setting of the diagnostic odyssey. Our analysis of this dataset showed that these known disease genes were not identified prior to WES enrollment for two key reasons: genetic heterogeneity associated with a clinical diagnosis and atypical presentation of known, clinically recognized diseases. What is becoming increasingly clear is that WES will be paradigm altering for patients and families with rare genetic diseases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".