Review of Factors Influencing Women's Choice of Mastectomy Versus Breast Conserving Therapy in Early Stage Breast Cancer: A Systematic Review
Bibliographic record
Abstract
We have performed a narrative synthesis. A literature search was conducted between January 2000 and June 2014 in 7 databases. The initial search identified 2717 articles; 319 underwent abstract screening, 67 underwent full-text screening, and 25 final articles were included. This review looked at early stage breast cancer in women only, excluding ductal carcinoma in situ and advanced breast cancer. A conceptual framework was created to organize the central constructs underlying women's choices: clinicopathologic factors, physician factors, and individual factors with subgroups of sociodemographic, geographic, and personal beliefs and preferences. This framework guided our review's synthesis and analysis. We found that larger tumor size and increasing stage was associated with increased rates of mastectomy. The results for age varied, but suggested that old and young extremes of diagnostic age were associated with an increased likelihood of mastectomy. Higher socioeconomic status was associated with higher breast conservation therapy (BCT) rates. Resident rural location and increasing distance from radiation treatment facilities were associated with lower rates of BCT. Individual belief factors influencing women's choice of mastectomy (mastectomy being reassuring, avoiding radiation, an expedient treatment) differed from factors influencing choice of BCT (body image and femininity, physician recommendation, survival equivalence, less surgery). Surgeon factors, including female gender, higher case numbers, and individual surgeon practice, were associated with increased BCT rates. The decision-making process for women with early stage breast cancer is complicated and affected by multiple factors. Organizing these factors into central constructs of clinicopathologic, individual, and physician factors may aid health-care professionals to better understand this process.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".