Comparing Breast Sensation between Alloplastic and Autologous Breast Reconstruction Patients Using the BREAST-Q Sensation Module
Bibliographic record
Abstract
BACKGROUND: Sensory recovery of the breast is a compelling frontier in comprehensive postmastectomy breast reconstruction. This study uses the BREAST-Q Sensation Module to evaluate the differences in women who underwent an alloplastic versus autologous breast reconstruction. METHODS: Women with a history of breast cancer and postmastectomy breast reconstruction were recruited through the Love Research Army, United States. Participants completed the BREAST-Q Sensation Module, which consists of three scales: Breast Symptoms, Breast Sensation, and Quality of Life Impact. Descriptive statistics and multiple linear regression analyses were used to compare outcomes between women undergoing alloplastic or autologous breast reconstruction. RESULTS: Of 1204 respondents, 933 were included for analysis: 620 (66.5 percent) underwent alloplastic reconstruction and 313 (33.5 percent) underwent autologous reconstruction. The average age and body mass index were 59.2 ± 10.1 years and 26 ± 5 kg/m 2 , respectively. Autologous reconstruction patients scored an average of 6.1 points (95 percent CI, 3.9 to 8.4; p < 0.001) and 5.3 points (95 percent CI, 2.5 to 8.1; p = 0.001) higher on the Breast Symptoms and Quality of Life Impact scales, respectively. No difference (0.0 points, 95 percent CI, -2.9 to 3.0; p = 0.75) was observed for the Breast Sensation scale. Increased time since reconstruction had a positive impact on Breast Symptoms scale scores. Radiotherapy negatively affected scores on both Breast Symptoms and Quality of Life Impact scales. CONCLUSIONS: Autologous breast reconstruction may be associated with fewer abnormal breast sensations and better sensation-related quality of life in comparison to alloplastic reconstruction. This information can be incorporated during preoperative patient counseling when discussing reconstructive options.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".