Fat Injection to Correct Contour Deformities in the Reconstructed Breast
Bibliographic record
Abstract
BACKGROUND: A ten-year, single-surgeon study of 37 patients from 1993 to 2003 who underwent fat injections to improve contour deformities in their reconstructed breasts was reviewed. METHODS: Fat was harvested from elsewhere in the body using a low-pressure syringe lipoaspiration system, washed gently with saline, and injected into depressions along the margins of reconstructed breasts. Blinded physician observers judged preoperative and postoperative photographs of breasts injected with fat and categorized the degree of contour improvement as substantial, minimal to moderate, or none. Complications of fat injections were noted. A total of 43 breasts in 37 patients were injected with autologous fat during 47 discrete events; some patients had the procedure repeated and some were treated bilaterally. Of the 43 treated breasts, 25 (58 percent) were reconstructed with implants, 17 (40 percent) were reconstructed with a TRAM (transverse rectus abdominis muscle) flap, and one (2 percent) was reconstructed with a TRAM and an implant. RESULTS: There were four complications (8.5 percent) in 47 treated breasts: one breast with cellulitis that resolved with antibiotics and three breasts with small, superficial lumps--two of which were biopsied and found to be liponecrotic cysts. Patient follow-up averaged 49 weeks, ranging from 3 weeks to 6 years. There was a substantial contour improvement in ten breasts (21 percent), minimal to moderate improvement in 30 breasts (64 percent), and no improvement in 7 breasts (15 percent). CONCLUSIONS: Although fat injection in and around the reconstructed breast has limitations, such as fat necrosis and need for repeated injections, our experience indicates that overall it is a very safe technique that can improve or correct significant contour deformities that otherwise would require more complicated, riskier procedures to improve.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".