Factors Influencing Surgical Treatment Decisions for Breast Cancer: A Qualitative Exploration of Surgeon and Patient Perspectives
Bibliographic record
Abstract
Background: The rate of mastectomy is much higher in Newfoundland and Labrador than in any other province in Canada, even for women diagnosed at an early stage. In this paper, we present qualitative data from women who have made a decision for surgical treatment and from breast surgeons in an effort to better explicate factors influencing breast cancer (bca) surgical decision-making. Methods: The study's descriptive, qualitative design involved holding interviews with breast surgeons and holding focus groups and interviews with women who were offered the choice of breast-conserving surgery (bcs) or mastectomy (mt). Results: Participants included 35 women and 13 surgeons. High interest in mt and increasing requests for prophylactic contralateral mt were evident. A host of factors-clinical, demographic, psychosocial, education-related, and cultural-influenced the decisions. A key factor for women was fear of recurrence and a need to "just get rid of it," but the experiences of others also influenced the decisions. Life stage and family considerations also factored prominently into women's decisions. Conclusions: Women with early-stage bca more often chose mt and often demanded prophylactic removal of the healthy breast. Findings highlight the importance of ensuring that women at average risk are appropriately counselled about the low likelihood of a subsequent contralateral bca and the lack of survival benefit associated with prophylactic contralateral mt. Findings also revealed other areas of presurgical discussion that might help women think through their personal circumstances and values so as to encourage informed surgical 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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".