Examining health beliefs: Why mastectomies are on the rise.
Bibliographic record
Abstract
192 Background: Rates of unilateral (UM) and contralateral prophylactic mastectomy (CPM) for early-stage breast cancer (ESBC) have been increasing. To understand this increase we sought the perspectives of patients who chose UM+/-CPM and treating surgeons. Methods: Applying the Health-Belief Model, we completed a qualitative study examining the surgeon’s practice and patient’s decision-making that resulted in the choice for mastectomy. Purposive sampling identified women across Toronto, Canada who were candidates for breast conserving therapy (BCT) but underwent UM+/-CPM. Academic and community breast surgeons from across Ontario, Canada and the United States were also recruited. Data were collected through semi-structured interviews, this continued until data saturation was reached. Constant comparative analysis identified key ideas. Results: 29 patients and 45 surgeons completed interviews. The dominant theme was a ‘misperceived threat’. Despite surgeons describing the high survivability of ESBC, patients greatly overestimated the threat of ESBC and strived to eliminate this threat by choosing mastectomy. Surgeons described BCT and UM as equivalent treatment options for ESBC, and often recommended BCT. In this average-risk population CPM was discouraged by the surgeons describing no survival advantage; despite this, women requested UM+CPM. Personal cancer experiences with family and friends were extremely influential in women’s request for UM+/-CPM. Previous negative experiences translated into an overestimated risk of recurrence, contralateral cancer, metastasisand subsequent death. Despite evidence-based consultations, patients misperceived the risks and severity of ESBC, and believed that by choosing UM+/-CPM they would live longer. Most women did not perceive any risks of undergoing mastectomy, yet many had ongoing issues with disturbed skin sensation, cosmesis and body image. Conclusions: A previous cancer experience was influential in the choice for UM+CPM. Despite surgeons counseling otherwise, women greatly overestimated the risk of ESBC and misperceived the benefits of mastectomy. Improved discussion of patient sources of information and fears around survival would benefit surgical consultations.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".