Detecting somatic mosaicism: considerations and clinical implications
Bibliographic record
Abstract
Human disease is rarely a matter of all or nothing; variable expressivity is generally observed. Part of this variability is explained by somatic mosaicism, which can arise by a myriad of genetic alterations. These can take place at any stage of development, possibly leading to unusual features visible at birth, but can also occur later in life, conceivably leading to cancer. Previously, detection of somatic mosaicism was extremely challenging, as many gold standard tests lacked the necessary resolution. However, with the advances in high-throughput sequencing, mosaicism is being detected more frequently and at lower levels. This raises the issue of normal variation within each individual vs mosaicism leading to disease, and how to distinguish between the two. In this article, we will define somatic mosaicism with a brief overview of its main mechanisms in concrete clinical examples, discuss the impact of next-generation sequencing technologies in its detection, and expand on the clinical implications associated with a discovery of somatic mosaicism in the clinic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".