The SAEORA Flap for Prosthetic Breast Reconstruction: A Novel Flap Design without the Use of Acellular Dermal Matrices
Bibliographic record
Abstract
Background: The gold standard for implant-based breast reconstruction uses acellular dermal matrices (ADMs). They provide improved inferolateral pole coverage, reduced capsular contracture rates, and increased primary expander fill volumes. However, ADMs are costly and have been associated with increased rates of postoperative infection, seroma, hematoma, implant malposition, and mastectomy flap necrosis (MFN). This study describes a novel autologous flap without the need of ADM, the serratus anterior external oblique rectus abdominis (SAEORA) flap, as an alternative in prosthetic-based breast reconstruction. Methods: A retrospective study was conducted on all patients who underwent SAEORA flap breast reconstruction by a single surgeon between January 1, 2013 and May 31, 2020 at a single institution. Patient demographics, diagnosis, treatment, tissue expander (TE) volume, implant size, complications, and results were assessed. Results: Forty-seven patients underwent 78 SAEORA flaps. Sixty-two had TEs placed, and 14 were direct-to-implant. Mean body mass index was 23.1 kg per m². Median primary TE fill volume was 150 mL, and final implant volume average was 450 mL. Mean follow-up was 14.5 months. Complications included infection/cellulitis (7.9%), seroma (6.6%), hematoma (5.2%), and MFN (7.9%). Conclusions: The SAEORA flap is a novel autologous flap and is a viable option for prosthetic-based breast reconstruction, with an acceptable complication profile relative to ADM-based reconstructions. Additionally, SAEORA is MFN-resistant and has been used effectively in salvage of exposed implants or ADM, and in double-bubble deformity correction.
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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 | 0.001 | 0.000 |
| 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".