BREAST-Q Patient-reported Outcomes in Different Types of Breast Reconstruction after Fat Grafting
Bibliographic record
Abstract
Background: Breast reconstruction after mastectomy improves patient quality of life. Independently of the type of reconstruction, ancillary procedures are sometimes necessary to improve results. Fat grafting to the breast is a safe procedure with excellent results. We report patient-reported outcomes using the BREAST-Q questionnaire after autologous fat grafting in different types of reconstructed breasts. Methods: We performed a single-center, prospective, comparative study that compared patient-reported outcomes using the BREAST-Q in patients after different types of breast reconstruction (autologous, alloplastic, or after breast conserving) who subsequently had fat grafting. Results: In total, 254 patients were eligible for the study, but only 54 (68 breasts) completed all the stages needed for inclusion. Patient demographic and breast characteristics are described. Median age was 52 years. The mean body mass index was 26.1 ± 3.9. The mean postoperative period at the administration of BREAST-Q questionnaires was 17.6 months. The mean preoperative BREAST-Q was 59.92 ± 17.37, and the mean postoperative score was 74.84 ± 12.48 (P < 0.0001). There was no significant difference when divided by the type of reconstruction. Conclusion: Fat grafting is an ancillary procedure that improves the outcomes in breast reconstruction independently of the reconstruction type and heightens patient satisfaction, and it should be considered an integral part of any reconstruction algorithm.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".