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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".