Medical Decision Making among Individuals with a Variant of Uncertain Significance in a Hereditary Cancer Gene and those with a CHEK2 Pathogenic Variant
Bibliographic record
Abstract
Despite national guidelines, women with a BRCA VUS or CHEK2 pathogenic variant are choosing to have risk-reducing surgeries such as bilateral mastectomies which are not aligned with their level of cancer risk based on genetic test results alone. Semi-structured telephone interviews were conducted with 6 women with a BRCA VUS and 12 with a CHEK2 pathogenic variant exploring the factors influencing their decision-making process when considering medical management options. Patients from a cancer registry agreed to a recorded telephone interview. Coding was performed using the main constructs from the Ottawa Patient Decision Guide including: knowledge, uncertainty, values, and support. Iterative analysis was used to identify emerging themes.\nAnalysis of the interviews revealed overlapping of the four constructs in the decision-making process. The knowledge sought to make medical management decisions was driven by the uncertainty associated with the genetic test results. Participants often contextualized their risk by building on the risk associated with genetic test results with family history, variant re-interpretation, and the knowledge that the risks associated with other genes may be higher. Patients generally made the decision they thought was best for them, even though it was more difficult if that decision was not supported by healthcare providers, friends, or family. When faced with uncertain cancer risks and presented with options for medical management, values were weighed against the negatives of each option. Often mental health was prioritized over the negatives associated with ‘removing body parts’.\nThese findings offer a look into the decisional needs of patients such as accurate knowledge, certainty, decisional support, and attention to personal values. Better understanding of the unmet needs of these patients and working to rectify them through provider education, outreach, counseling strategies to mitigate uncertainty, and research on how to best address and identify each patient’s specific decisional needs can contribute to the goal of risk-appropriate and values-based decision-making. With a better understanding of patients’ decisional needs, healthcare providers can better advocate for tailored counseling sessions which explore and address specific patient needs to help them make informed, risk-appropriate, and value-based medical management decisions.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".