The Use of Lipofilling to Treat Congenital Hypoplastic Breast Anomalies
Bibliographic record
Abstract
BACKGROUND: Treatment options for congenital hypoplastic breast anomalies are often open, including radial scoring, parenchymal flaps, and insertion of expanders and implants. Drawbacks of open techniques involve scarring, the use of drains, and inpatient stays. The use of lipofilling to treat breast deformities is increasing, as more research is completed in this area. PATIENTS AND METHODS: We report a retrospective study of 10 patients below the age of 20 following autologous fat transfer between January 1, 2003 and January 1, 2004. (2 Poland syndrome, 3 bilateral tuberous breast, and 5 unilateral micromastia). Age, cup size, the number of sessions, time interval between each session, volumes injected, and complications were recorded. Postoperative mammography, ultrasonography, and MRI were assessed by a specialized radiologist. Patients answered a questionnaire 1 year after the procedure. RESULTS: Mean follow-up was 68 months (60-77 months) and mean age was 17.5 years (15-20 years). Mean number of fat injection sessions was 2 (1-4) and mean volume injected 285 mL per breast (200-500 mL). The time interval between each session was 5 months (3-6 months). Cup size remained unchanged after at least 5 years of follow-up. One case underwent a contralateral breast reduction. The cosmetic results considered satisfactory in almost all the patients after 1 year of follow-up. None of our patients complained of scars or defects at the donor site. All breasts imaging were normal except 1 patient with oil cysts. CONCLUSION: Our preliminary results using lipofilling to treat young patients with breast hypoplasia with lipofilling are very encouraging. The authors believe it is an alternative of choice for the correction of the young woman's breast deformities if the avoidance of scarring is preferred.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".