Autogenous breast reconstruction for total mastectomies: a narrative review
Bibliographic record
Abstract
Background and Objective: Mastectomies have a significant socio-psychological impact, motivating patients to undergo breast reconstruction. Initially, silicone implants were used to reconstruct the breast. However, breast implants have been the subject of successive crises throughout the years. Indeed, rupture, silicone bleeding, and capsular contracture remain topical. In 2019, the BIOCELL textured breast implants was banned and recalled due to the discovery of the breast implant-associated anaplastic large cell lymphoma (BIA-ALCL). More recently, the breast implant illness has been depicted in the media. To cope with these issues and to respond to some patients' expectations for a natural reconstruction, plastic surgeons have developed autogenous solutions for breast reconstruction. Since Taylor's research on angiosomes, the development of the microsurgery and more recently fat grafting, autogenous breast reconstruction has known a tremendous expansion. Autologous breast reconstruction allows a more natural feeling and texture. This narrative review aims to provide to the readers a comprehensive and updated evidence-based overview of state of the art about autologous breast reconstruction after total mastectomy. Methods: We conducted a narrative review of the literature searching for papers published between January 2010 and December 2022. The MeSH terms with different combinations were used to identify articles for inclusion. After screening article titles and abstracts independently by three authors, 66 papers were included in this review. Key Content and Findings: In this review, the authors describe and discuss the different autogenous techniques in breast reconstruction. Conclusions: Autologous reconstructions provide very satisfactory, durable, and reliable results with relatively low complication rates. Deep inferior epigastric perforator (DIEP) flaps, latissimus dorsi flaps and autologous fat grafting are the most common type of autogenous breast reconstructions.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".