Incidental Findings from Clinical Genome‐Wide Sequencing: A Review
Bibliographic record
Abstract
There are several unresolved challenges associated with the clinical application of genome-wide sequencing technologies. One of the most discussed issues is incidental findings (IF), which are defined as discoveries made as a result of genetic testing that are unrelated to the indication for the test. The discussion surrounding IF began in the context of research, which we have used to frame consideration of IF in the clinical context. There is growing consensus that analytically valid and medically actionable IF should be offered to patients, but whether and to what extent clinicians should disclose other kinds of IF is debated. While others have systematically reviewed the literature concerning genetic IF, previous reviews focus on ethical and research-related issues and do not consider the implications for the genetic counseling profession specifically. This review discusses the practical considerations, ethical concerns and genetic counseling issues related to IF, with a particular focus on clinical genome-wide sequencing. To date, the bulk of the literature with respect to IF in the clinical context consists of commentaries, reviews and case reports. There is a need for more empirical studies to provide a foundation for institutional protocols and evidence-based clinical practice standards.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".