Vertical Scar Breast Reduction
Bibliographic record
Abstract
BACKGROUND: The vertical scar bilateral breast reduction is a highly effective technique to reduce breast volume and create long-lasting aesthetic improvements. A cited disadvantage is the inability to adequately shorten the vertical scar, leading to chest wall scars or inframammary puckers. Gathering or cinching sutures have been described as a strategy to confront this issue. This article aims to determine if suture gathering is an effective methods to (1) reduce the incision length, (2) shorten the areola-to-inframammary fold (IMF) distance, and (3) reduce the pucker revision rate. METHODS: All patients undergoing vertical breast reduction performed by the senior author (E.H.F.) from 2001 to 2007 were included. The patient population was divided into "gather" and "no gather" groups depending on how the vertical incision was closed. RESULTS: There were 203 patients in the "no gather" group and 193 in the "gather" group. Age, body mass index, and resection weight were statistically but not clinically different. The percent reduction in vertical incision length was significantly greater in the "gather" group (34.2 ± 9.9% vs. 12.2 ± 5.9%). Both groups showed a gradual lengthening of areola-to-IMF distance postoperatively. Suture gathering had no impact on the pucker revision rate but increased healing complications. CONCLUSION: Gathering sutures significantly reduce the incision length in the operating room but do not change the areola-to-IMF distance or pucker revision rate. Gathering negatively influences skin vascularity and wound healing. It is acceptable and necessary to have a longer areola-to-IMF distance in a vertical reduction to accommodate increased projection.
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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 | 0.001 |
| 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) | 0.006 | 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".