Preferences for breast cancer prevention among BRCA mutation carriers
Bibliographic record
Abstract
17018 Purpose: Breast cancer screening with MRI is a new option available to patients with BRCA 1/2 mutations. We analyzed preferences for this modality and 10 other breast cancer- related health states and preventive measures among women without cancer or known high risk and women with BRCA mutations. Methods: Following IRB approval, we administered a time trade-off questionnaire to mutation carriers and to women without breast cancer or known high risk. We used Kruskal-Wallis test to compare the two groups with respect to continuous variables, chi-square tests to compare proportions, and the Wilcoxon signed rank test for pairwise comparisons. We then developed logistic regression models to analyze the association of mutation carrier status and demographic factors with willingness to trade time for each of the 11 health states. Results: Two-hundred-four women (44 mutation carriers and 160 without breast cancer or known high risk) responded to the questionnaire. Both groups assigned the highest preference rating to mammography and the next-highest to MRI, but the differences in ratings were not statistically significant. Both groups assigned the lowest preference ratings to having a child with a mutation and the next lowest to ovarian cancer. In pairwise comparisons, both groups ranked oophorectomy higher than ovarian cancer (p <0.01), but mutation carriers did not rank prophylactic mastectomy significantly differently from breast cancer (p=0.38). In the logistic regression models, mutation carrier status was not a statistically significant predictor of willingness to trade time for any health state, but younger age, lower income, and nonwhite race/ethnicity were associated with willingness to trade time for certain health states. Conclusion: Our data indicate that MRI is as acceptable as mammography to respondents, and that the preferences of BRCA 1/2 mutation carriers are similar to those of other women. Age and other demographic factors may be more important than mutation status in determining preferences. The preference ratings of individuals should not be inferred from demographic characteristics or mutation status. However, such ratings can help to clarify the quality of life implications of clinical decision-making and health care policy regarding breast cancer prevention. No significant financial relationships to disclose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".