Factors associated with the choice of surgery in breast cancer : a systematic review
Bibliographic record
Abstract
Background: Patients with early stage breast cancer having more aggressive surgery have been reported by several studies. Some studies from US also reported that there is an increasing trend in the use of mastectomy. A study even showed that there is 150% increase in bilateral mastectomy rate using data from Surveillance, Epidemiology, and End Results registries (SEER). The increasing use of mastectomy leads to the concerns about reasons behind the decision of surgery type. The objective of this literature review is to identify the factors which would affect the choice of surgery. We will make recommendations on guideline, implementation and the use of appropriate surgery, to prevent the unnecessary mastectomy. \n \nMethods: Literature search of articles was conducted using several database including PubMed, MEDLINE and Google Scholar. The keywords used were \n“Mastectomy rate” AND “breast cancer”, “Breast surgery choice” AND "factor", “Breast conserving surgery” AND “choice”. The periods were limited to 1990-2013. \n \nResults: Of 4335 articles identified, 11 studies were found to be relevant to the review. These studies were from different countries with different sample sizes, analysis method and study designs. The rate of mastectomy was widely varied across countries. The rate was clearly lower in western countries, such as Canada, UK and US, while in Asia like Hong Kong and Turkey, the rate is much higher. All of the reviewed studies evaluated different factors, which can influence the choice of treatment. These factors can be broadly categorized as demographics, clinical data, body image and sexuality, surgeon and psychological effects. Factors about age, marital status, family history, tumor size, histological type, nodal status, body image, fears of recurrence, further treatment and dying from cancer and surgeons were included in different studies found to have significant effect on individual in decision of surgery type. \n \nConclusion: Both patient and surgeon play an important role in the selection of surgery. The evidence suggests that reducing unknown bias of surgeons and concerns from patients can help choosing the optimal surgery type. Adequate communication and information are necessary for patient in making the decision on treatment.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".