Unsolved challenges in pediatric whole-exome sequencing: A literature analysis
Bibliographic record
Abstract
Whole-exome sequencing (WES) has been instrumental in the discovery of novel genes and mechanisms causing Mendelian diseases. While this technology is now being successfully applied in a number of clinics, particularly to diagnose patients with rare diseases, it also raises a number of ethical, legal and social issues. In order to identify what challenges were directly foreseen by technology users, we performed a systematic review of the literature. In this paper, we focus on recent publications related to the use of WES in the pediatric context and analyze the most prominent challenges raised by technology users. This is particularly relevant considering that a) most patients currently undergoing testing using WES to identify the genetic basis for rare diseases are children and b) their lack of capacity to consent for themselves makes them a vulnerable population and generates the need for specific ethical, legal and regulatory procedures. We identified key challenges that related to four main categories: (1) intake; (2) sequence production and analysis; (3) reporting of results and counseling considerations and (4) collaborative data interpretation and data sharing. We then contextualize these challenges in light of the recent recommendations and guidelines, published by professional societies that have significant potential to impact the field.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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".