The clinical utility of whole‐exome sequencing in the context of rare diseases – the changing tides of medical practice
Bibliographic record
Abstract
Whole-exome sequencing (WES) carries the potential to facilitate the identification of disease causing genes. This is particularly relevant concerning rare diseases, which proves particularly difficult for physicians to diagnose. However, the complexity of this technology renders its applicability onto the clinical setting uncertain. Our study thus aims to understand physicians' perspectives regarding the clinical utility of WES, particularly for providing a diagnosis for patients with rare diseases. Ten semi-structured interviews were conducted with physicians with experience and familiarity with WES, and the major themes that emerged from our interviews were (i) the relevance of WES in diagnosing patients with rare diseases (appropriateness); (ii) the cost-effectiveness of WES (accessibility), (iii) the practical issues related to the clinical implementation of WES (practicability); and (iv) ethical, legal and social issues (acceptability). Our study highlights how the clinical implementation of WES presents additional challenges where rare diseases are taken into consideration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".