Total Breast Reconstruction with Fat Grafting Combined with Internal Tissue Expansion
Bibliographic record
Abstract
Summary: Breast reconstruction procedures are currently performed as standard practice and are an integral part of breast cancer treatment. The advantages and disadvantages of particular types of reconstruction are well known. Most of them require the woman to accept a different consistency of the reconstructed breast, as is the case with implants, or to have extra scarring in the donor site and a cutaneous island with a different texture in the recipient site, as is the case with TRAM, DIEP, and other flaps. This article presents the concept of breast reconstruction with fat grafting combined with internal tissue expansion. A 44-year-old woman after a right mastectomy for invasive carcinoma T1c, N0 (IIB) was presented. After unsatisfactory treatment with fat grafting supported by BRAVA system, she was qualified for breast reconstruction with fat grafting combined with tissue expansion. An anatomic 350 cm3 breast expander with an integrated port was implanted. It was filled with saline solution up to volume of 380 ml. Then, 7 fat grafting procedures combined with gradual emptying of the expander were performed. The 50/70 principle was used, that is, 50 ml of physiological saline was removed from the expander and 70 ml of fat was injected into the subcutaneous tissue over the expander. Finally, the expander was removed and a satisfactory volume and completely natural consistency of the breast was obtained. Breast reconstruction with fat grafting combined with tissue expansion is a promising method of total breast reconstruction after mastectomy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 0.001 |
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".