Mammographic Changes after Fat Transfer to the Breast Compared with Changes after Breast Reduction
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Bibliographic record
Abstract
BACKGROUND: : One issue in the adoption of autologous fat transfer to the breast is concern over mammographic changes that may obscure cancer detection. The authors compared mammographic changes following fat grafting to the breast with changes seen after breast reduction. METHODS: : Twenty-seven women who had normal preoperative mammograms were treated with fat grafting to the breast, including admixing of autologous adipose stem cells with the fat graft, for cosmetic augmentation. Repeated mammograms were performed 12 months after surgery. As a control group, postsurgical mammograms from 23 reduction mammaplasty patients were compared. Eight academic breast imaging radiologists reviewed each mammogram in a blinded fashion. Outcomes analysis accounting for individual radiologist's tendencies was performed using generalized estimating equations. RESULTS: : The average volume of fat injected per patient was 526.5 cc. Fifty mammograms (27 lipotransfer, 23 breast reduction) were assessed. Differences in interpretation among individual radiologists were consistently observed (p < 0.10). Differences in abnormality rates were nonsignificant for oil cysts, benign calcifications, and calcifications warranting biopsy. Scarring (p < 0.001) and masses requiring biopsy (p < 0.001) were more common in the reduction cohort. Breast Imaging Reporting and Data System scores were higher after breast reduction (p < 0.001). Significant differences in the recommended follow-up time were also seen (p < 0.01). CONCLUSIONS: : Compared with reduction mammaplasty, a widely accepted procedure, fat grafting to the breast produces fewer radiographic abnormalities with a more favorable Breast Imaging Reporting and Data System score and less aggressive follow-up recommendations by breast radiologists. CLINICAL QUESTION/LEVEL OF EVIDENCE: : Therapeutic, III.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it