Intrafamilial disclosure of risk for hereditary breast and ovarian cancer: points to consider
Bibliographic record
Abstract
The primary goal of breast and ovarian cancer screening is to minimize the cases of advanced disease and therefore its mortality rate. For hereditary breast and ovarian cancer, one method to reach this goal is to disseminate genetic risk information among family members. However, experience tells us that this information does not always reach family members in a timely manner, if at all. There are many moving parts to a decision to disclose genetic risk information within a family, and the lack of detail and cohesion in current guidelines do a disservice to hereditary breast cancer prevention. Utilizing legal, medical, and policy databases for literature, case law and policy documents relating to communication of genetic test results within families, as well as a consultative process with representative stakeholders, a points to consider has been developed to address a number of issues that might impact the ability and willingness of patients to inform family members of genetic risk. These include: what is "genetic information"; who is the "family"; why should patients inform their family members; and how should health professionals be involved in this process? This represents only an initial step towards fostering better communication within families. Additional research is needed to determine the best methods for encouraging this communication and motivations for disclosing or not and to promote the development of a solution, considering the complexity of human relationships and the probabilistic nature of genetic information.
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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 | 0.047 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".