Genetic counseling and interpretation of genetic tests in familial adenomatous polyposis and hereditary nonpolyposis colorectal cancer
Bibliographic record
Abstract
PURPOSE: Genetic counseling and interpreting genetic test results can be complex. Moreover, without knowing the limitations of the methods used and the lifetime probability of developing cancer in individuals who carry a gene that predisposes to cancer, misinterpretation may lead to false assurance. The purpose of this review is to discuss how genetic counseling will benefit patients and their family, the genetic tests available for hereditary colorectal cancer syndromes, and the interpretation of results. METHODS: Current literature was reviewed and our clinical and research experiences were incorporated. RESULTS: This review serves as a guide to enable various health care providers to better counsel patients in their quest for advice on prevention, early detection, and surveillance for colorectal cancer. Notable topics of discussion are who should undergo genetic counseling and consider testing and how the interpretation of test results can be misleading; for example, understanding the difference between a no mutation detected vs. a negative test result. CONCLUSIONS: Genetic counseling is of paramount importance for patients to fully understand the limitations of genetic testing and will aid in the management of patients who are susceptible to colorectal cancer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".