Vertical Scar Reduction Mammaplasty: A 15-Year Experience Including a Review of 250 Consecutive Cases
Bibliographic record
Abstract
BACKGROUND: Vertical scar reduction mammaplasty has the advantage of reduced scar burden and improved long-term projection of the breasts. The technique has been criticized for being restricted to cases of mild to moderate mammary hypertrophy and is considered more intuitive and difficult to learn when compared with more conventional inverted-T scar reduction mammaplasties. This article describes the technique used in the largest reported series of vertical scar reduction mammaplasties performed by a single surgeon. METHODS: The technique performed in this series uses a mosque dome skin marking pattern; transposition of the nipple-areola complex on a superior or medial dermoglandular pedicle, depending on its position with respect to the skin markings; an excision en bloc of skin, fat, and gland; postexcision liposuction; and wound closure in two planes, with gathering of the skin of the vertical wound. A chart review of 250 consecutive patients treated between November of 2000 and December of 2003 was performed. RESULTS: The average reduction per breast (including liposuction) was 636 g (range, 60 to 2020 g). Complications were minimal (5.6 percent of breasts), with no nipples being lost, attesting to the safety of this technique. CONCLUSIONS: This technique for vertical scar reduction mammaplasty has been applied to breast reductions of all sizes and has consistently produced good breast shape, with an operation that is shorter to perform and leaves less scarring than standard breast reductions. This technique is straightforward and easy to learn, and offers a safe, effective, and predictable way for treating mammary hypertrophy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".