Development of a New Patient-Reported Outcome Measure for Breast Surgery: The BREAST-Q
Bibliographic record
Abstract
BACKGROUND: Measuring patient-reported outcomes has become increasingly important in cosmetic and reconstructive breast surgery. The objective of this study was to develop a new patient-reported outcome measure to assess the unique outcomes of breast surgery patients. METHODS: Patient interviews, focus groups, expert panels, and a literature review were used to develop a conceptual framework and a list of questionnaire items. Three procedure-specific questionnaires (augmentation, reduction, and reconstruction) were developed and cognitive debriefing interviews used to pilot each questionnaire. Revised questionnaires were field tested with 1950 women at five centers in the United States and Canada (response rate, 72 percent); 491 patients also completed a test-retest questionnaire. Rasch measurement methods were used to construct scales, and traditional psychometric analyses, following currently recommended procedures and criteria, were performed to allow for comparison with existing measures. RESULTS: The conceptual framework included six domains: satisfaction with breasts, overall outcome, and process of care, and psychosocial, physical, and sexual well-being. Independent scales were constructed for these domains. This new patient-reported outcome measure "system" (the BREAST-Q) contains three modules (augmentation, reconstruction, and reduction), each with a preoperative and postoperative version. Each scale fulfilled Rasch and traditional psychometric criteria (including person separation index 0.76 to 0.95; Cronbach's alpha 0.81 to 0.96; and test-retest reproducibility 0.73 to 0.96). CONCLUSIONS: The BREAST-Q can be used to study the impact and effectiveness of breast surgery from the patient's perspective. By quantifying satisfaction and important aspects of health-related quality of life, the BREAST-Q has the potential to support advocacy, quality metrics, and an evidence-based approach to surgical practice.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".