Cancers related to genetic mutations: important psychosocial issues for Canadian family physicians.
Bibliographic record
Abstract
OBJECTIVE: To review psychosocial issues family physicians might wish to be aware of when discussing genetic testing for predisposition for cancer with their patients. QUALITY OF EVIDENCE: Articles from academic journals were reviewed. Studies provided level II and III evidence. MAIN MESSAGE: Family physicians should be prepared to explore their patients' decisions for or against genetic testing, as well as to discuss the possible outcomes of a decision to test. While genetic testing has many potential benefits, patients are at risk of having psychosocial problems at many stages in a genetic testing inquiry. To minimize these problems, family physicians should discuss motivation for testing and the potential psychosocial effect of both deciding to undergo and deciding to forgo genetic testing for cancer-related genes. Also important are deciding whether patients qualify for the tests; coping with the waiting period before testing can be done; and discussing positive, negative, and inconclusive outcomes of testing. CONCLUSION: Family physicians are likely in the best position to discuss genetic testing for predisposition for cancer with their patients given their knowledge of both the tests and their patients' ability to cope with testing.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".