Misperceptions Lead to Increase in Mastectomy for Early-Stage Breast Cancer
Bibliographic record
Abstract
FigureSAN FRANCISCO—A disconnect exists between the information being provided by surgeons to women with newly diagnosed early-stage breast cancer, and the information those women use to make their decisions regarding surgery, according to a study reported here at the Breast Cancer Symposium (Abstract 75). Andrea M. Covelli, MD, PhD, a general surgery resident at the University of Toronto who conducted the study at the university's Institute of Health Policy Management and Evaluation, said that despite surgeons counseling otherwise, some women greatly overestimate the risk of early-stage breast cancer and misperceive the benefits of mastectomy. In an average-risk population, surgeons discouraged contralateral prophylactic mastectomy as offering no survival advantage, the study showed. Despite this, women still requested unilateral and contralateral prophylactic mastectomy. “Surgeons said they described the high survivability of early-stage breast cancer, and told patients that breast-conserving therapy and unilateral mastectomy were equivalent treatment options for early-stage breast cancer, and strongly advised against contralateral prophylactic mastectomy,” Covelli said. “But even so, patients greatly overestimated the threat of death from their cancer and tried to eliminate this threat by choosing unilateral mastectomy, with or without contralateral prophylactic mastectomy. The treatment discussion wasn't that pivotal to the women in their decision making. Patients' risk perceptions were more shaped by the cancer experiences of family and friends, and those negative experiences translated into an overestimated risk of recurrence, contralateral cancer, metastasis, and subsequent death.” Study Details The researchers interviewed 29 non-high-risk patients in Toronto: 15 were suitable candidates for breast-conserving therapy but had unilateral mastectomy; and 14 who also had no indication for contralateral prophylactic mastectomy but underwent that procedure anyway. Also interviewed were 45 surgeons: approximately half were in academic and half in community practice; half were general or breast surgeons and half were surgical oncology specialists; and half were from Ontario, Canada, and half from the United States. The Health-Belief Model was applied identifying factors influential in the choice for unilateral mastectomy, with or without contralateral prophylactic mastectomy. “Patients misperceived the severity of early-stage breast cancer, and believed that by choosing unilateral mastectomy with or without contralateral prophylactic mastectomy they would live longer, and ‘never have to go through this again,’” Covelli explained. Most women did not perceive any risks of undergoing mastectomy, even though many had ongoing issues with skin sensation, cosmesis, and body image. Hearing about Other Patients' Experiences Covelli said women may benefit from learning about other patients' postoperative experiences regarding the more extensive surgeries. Since undergoing unilateral mastectomy and contralateral prophylactic mastectomy is not without risks, improved discussion of patient sources of information and fears around survival may benefit surgical consultations, facilitating informed decision-making, Covelli said. “This isn't about making women make the ‘correct’ decision, but rather, making a well-informed decision.” ‘Emotional Level’ Asked for his perspective, Don S. Dizon, MD, Clinical Co-Director of the Department of Gynecologic Oncology at Massachusetts General Hospital Cancer Center, said: “We're still trying to grapple with an emotive response, and it seems like we're talking in two different planes. You can talk about data and you can talk about what we learned about survival, between breast conservation and mastectomy, but there's also that emotional level that we need to start addressing far more.” Women are going to come to their own conclusions based on their own experiences, “and if their experience with their mom, their sister, their best friend was not a good one—for example: ‘My sister who was 30 had a lumpectomy, and she relapsed three years later, and now she's dead’—that's all going to be informing her decision. “The woman might say ‘I don't want to die of this, and I want to reduce my risk as much as possible. Hence, I don't care what you tell me, I'll go with a mastectomy.’” Dizon said there is a need to inform patients not on the data side, but on the emotional side, narrative for narrative: For example: “Yes it's true your sister died of breast cancer at age 30, but I can show you four other patients who are also 30, and they still have both breasts, and they're fine.”
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".