Chest Wall Perforator Flaps in Breast Conservation: Versatile, Affordable, and Scalable: Insights from the Largest Single-Surgeon Audit from India
Bibliographic record
Abstract
Chest wall perforator flaps (CWPFs) are a promising option for partial breast reconstruction but are underutilized, particularly in resource-limited settings. This retrospective observational study explores the feasibility and impact of CWPFs in breast-conserving surgery at our single-surgeon center, where 203 procedures were performed between 2018 and 2023. We evaluate 200 cases treated after multidisciplinary tumor board discussions and shared decision-making, assessing clinicopathological data, surgical outcomes, oncological results, cosmetic outcomes, and patient-reported outcome measures (PROMs). The median age of patients was 52.5 years. Single CWPFs were used in 75.9% and dual flaps in 24.1%. Sentinel node biopsy was performed in 76.9% of malignant cases, with no positive margins. Minor complications occurred in 11%, and no major complications were reported. At a 27-month median follow-up, the overall survival rate was 97.5%, with a disease-free survival of 92.1%. Cosmetic outcomes were good-to-excellent, and PROMs indicated high satisfaction. This largest single-surgeon study from Asia demonstrates the transformative role of CWPFs in breast conservation surgery for Indian women with sizable, locally advanced tumors. The technique offers excellent oncological and cosmetic outcomes, reduced costs, and a shorter operative time, highlighting the need for oncoplastic algorithms in resource-limited settings to improve breast conservation accessibility.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".