Genetic Testing for Cancer Predisposition
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
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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