Genetic testing for hereditary cancer syndromes: patient recommendations for improved risk communication
Bibliographic record
Abstract
BACKGROUND: Multi-gene panel testing is replacing single-gene testing for patients with suspected hereditary cancer syndromes. The detection of a hereditary cancer syndrome allows tested individuals to initiate enhanced primary and secondary prevention efforts-where available-with a view to reduce disease burden. Current policy prevents testing programmes from communicating genetic test results with potentially affected family members, yet it is well documented that tested individuals face multiple challenges in initiating such discussions with relatives. OBJECTIVE: In response to this challenge, we sought patient recommendations about how to improve genetic risk communication to enhance interfamilial discussions about primary and secondary disease prevention. DESIGN: We conducted 25 semi-structured interviews with individuals who received genetic testing through British Columbia's Hereditary Cancer Program between 2017 and 2018. Interviews were professionally transcribed and analysed using a constant comparative approach. RESULTS: Participants described difficulty engaging in conversations with relatives who were resistant to receiving genetic risk information, when communicating with younger relatives and where participants reported strained familial relationships. Participants recommended that testing facilities provide a summary of results and implications and that resources be made available to prepare patients for challenging discussions with family members. DISCUSSION: Our study demonstrates that individuals undergoing genetic testing for suspected hereditary cancer syndromes would benefit from additional supportive resources alongside genetic counselling. Providing this on-going support will enhance the accurate and transparent communication of risk to facilitate the uptake of cascade testing and enhanced prevention strategies.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".