A Scoping Review of the Application of BREAST-Q in Surgical Research
Bibliographic record
Abstract
Background: Collection of patient-reported outcome (PRO) data can facilitate cost-effective, evidence-based, and patient-centered care. The BREAST-Q has become the gold standard tool to measure PRO data in breast surgery. The last review of its application indicated that it was underutilized. Considering the evolution in breast surgery, the purpose of this study was to perform a scoping review of BREAST-Q application since 2015 and identify emerging trends and potential persistent gaps to guide patient-centered practice and future research in breast surgery. Methods: We performed an electronic literature review to identify publications published in English that used the BREAST-Q to assess patient outcomes. We excluded validation studies, review papers, conference abstracts, discussions, comments, and/or responses to previously published papers. Results: We identified 270 studies that met our inclusion criteria. Specific data was extracted to examine the evolution of the BREAST-Q application and examine clinical trends and research gaps. Discussion: Despite a significant increase in BREAST-Q studies, gaps in the understanding of the patient experience remain. The BREAST-Q is uniquely designed to measure quality of life and satisfaction with outcome and care. The prospective collection of center-specific data for every type of breast surgery will generate important information for the provision of patient-centered and evidence-based care.
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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 | 0.053 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.038 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".