Genetic Testing for Cancer Predisposition
Bibliographic record
Abstract
Clinical cancer genetics is becoming an integral part of the care of cancer patients. This review describes the clinical aspects, genetics, and clinical genetic management of most of the major hereditary cancer susceptibility syndromes. Multiple endocrine neoplasia type 2, von Hippel-Lindau disease, and familial adenomatous polyposis are examples of syndromes for which genetic testing to identify at-risk family members is considered the standard of care. Genetic testing for these syndromes is sensitive and affordable, and it will change medical management. Cancer genetic counseling and testing is probably beneficial in other syndromes, such as the hereditary breast cancer syndromes, hereditary nonpolyposis colorectal cancer syndrome, Peutz-Jeghers syndrome, and juvenile polyposis. There are also hereditary cancer syndromes for which testing is not yet available and/or is unlikely to change medical management, including Li-Fraumeni syndrome and hereditary malignant melanoma. Thorough medical care requires the identification of families likely to have a hereditary cancer susceptibility syndrome for referral to cancer genetics professionals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".