Single-Stage Suction-Assisted Lipectomy with Dermal Mastopexy: An Alternative Procedure in Repeated Reduction Mammaplasty with Questionable Nipple-Areola Complex Vascularity
Bibliographic record
Abstract
I am writing to express my displeasure with the authors and reviewers of “Single-Stage Suction-Assisted Lipectomy with Dermal Mastopexy: An Alternative Procedure in Repeated Reduction Mammaplasty with Questionable Nipple-Areola Complex Vascularity.”1 This is not a new technique. The literature review was incomplete. I published this technique in the Aesthetic Surgery Journal in 2006, with photographs. I applaud them, however, for accurately implementing my published techniques for lipo-reduction. What is particularly troubling, however, is that the editorial staff appear to have signed off on publishing something and calling it “new,” without making any reference to a very similar article published in the Aesthetic Surgery Journal in 2006. I have presented my work with liposuction breast reduction at two meetings of the Lipoplasty Society of North America (in Dallas in 1996 and in San Francisco in 1997), with a poster exhibit at the American Society of Plastic and Reconstructive Surgeons meeting in New Orleans in 2000; multiple times at meetings of the American Society for Aesthetic Plastic Surgery in New York, Vancouver, and Boston; multiple teaching courses at the Aesthetic Society and the American-Brazilian Aesthetic meeting in Park City in 2019 featuring mastopexy technique; and the New England meeting in 2022 including mastopexy. It will also be presented at the International Society of Aesthetic Plastic Surgery meeting this September in Istanbul, Turkey. The International Society of Aesthetic Plastic Surgery is the world’s leading professional body for board-certified aesthetic plastic surgery. I described this very technique for liposuction breast reduction with concurrent mastopexy in 2006 in both primary and secondary cases. Furthermore, I have performed over 1000 reductions with concurrent mastopexy while also managing to avoid compromised circulation. I began to offer mastopexy first, in 2002, to all patients undergoing liposuction breast reductions with me; overall, approximately 75% decide to have the mastopexy performed concurrently. Although the authors do not comment on scars, I have found they are improved compared with standard reductions, because the maximum tension during surgery rapidly dissipates; it does not increase. I have not needed any scar revisions. DISCLOSURE The author has no financial interest to declare in relation to the content of this communication. Lawrence Gray, MDAtlantic Plastic Surgery100 Griffin Road, Suite BPortsmouth, NH 03801[email protected]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".