Attitudes and Decisional Conflict Regarding Breast Reconstruction Among Breast Cancer Patients
Bibliographic record
Abstract
BACKGROUND: The decision to undergo breast reconstruction (BR) surgery after mastectomy is made during stressful circumstances. Many women do not feel well prepared to make this decision. OBJECTIVE: Using the Ottawa Decision Support Framework, this study aims to describe women's reasons to choose or not choose BR, BR knowledge, decisional preparedness, and decisional conflict about BR. Possible demographic, medical, BR knowledge, and attitudinal correlates of decisional conflict about BR were also evaluated. METHODS: Participants were 55 women with early-stage breast cancer drawn from the baseline data of a pilot randomized trial evaluating the efficacy of a BR decision support aid for breast cancer patients considering BR. RESULTS: The most highly ranked reasons to choose BR were the desire for breasts to be equal in size, the desire to wake up from surgery with a breast in place, and perceived bother of a scar with no breast. The most highly ranked reasons not to choose BR were related to the surgical risks and complications. Regression analyses indicated that decisional conflict was associated with higher number of reasons not to choose BR and lower levels of decisional preparedness. CONCLUSIONS: The results suggest that breast cancer patients considering BR may benefit from decisional support. IMPLICATIONS FOR CLINICAL PRACTICE: Healthcare professionals may facilitate decision making by focusing on reasons for each patient's uncertainty and unaddressed concerns. All patients, even those who have consulted with a plastic surgeon and remain uncertain about their decision, may benefit from decision support from a health professional.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".