Detecting somatic mosaicism: considerations and clinical implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it