Breast Cancer Surgical Treatment Choices in Newfoundland and Labrador, Canada: Patient and Surgeon Perspectives
Bibliographic record
Abstract
BACKGROUND: Breast cancer remains the second-leading cause of cancer death among Canadian women. Treatment for breast cancer often includes surgery. Many women have a choice between mastectomy (MT; removal of the entire breast) or breast conserving surgery (BCS; removal of the tumour and some noncancerous breast tissue) followed by radiation. However, Newfoundland and Labrador consistently has a higher rate of mastectomies than the rest of Canada. In this project, we aim to better understand that trend. DESIGN AND METHODS: A multi-method design was chosen. Surgical treatment data kept by the province will be examined to describe the number and types of breast cancer surgeries over time. Second, we will hold focus groups with women around the province who have made surgical treatment choices to explore influences on their decisions. Finally, semi-structured interviews with breast cancer surgeons and surgical residents will explore their opinions on surgical treatment choices. EXPECTED IMPACT FOR PUBLIC HEALTH: Cancer treatment choices are complex decisions, affected by clinical, demographic and social variables. Understanding why women from Newfoundland and Labrador have the highest rate of mastectomy in Canada is critical to ensure they are receiving appropriate screening and care. Greater understanding of the influences on women's surgical choices may encourage informed decisions amongst women and physicians and promote active communication about treatment, benefits relevant to all jurisdictions and health authorities. Further, if factors such as geographic proximity to treatment facilities are associated with treatment decisions, this information is important for public health screening and service planners.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".