Longevity of ptosis correction in mastopexy and reduction mammaplasty: A systematic review of techniques
Bibliographic record
Abstract
Background: Mastopexy and reduction mammaplasty are commonly performed procedures in plastic surgery with many variations in incision pattern, pedicle design, and additional support maneuvers. Aesthetically pleasing on table results are widely accomplished; however, the longevity of the outcome and sustained correction of ptosis or pseudoptosis is not universal. A systematic review of mastopexy and reduction mammaplasty procedures was performed to investigate which techniques provided the greatest long-term correction of ptosis. Methods: A broad search of the literature was performed using the PubMed database from inception to December of 2021. Study characteristics, number of patients, number of breasts, technique, outcome, and average follow-up time were extracted for analysis. Study quality was assessed using the Newcastle-Ottawa Scale when applicable. Results: The primary search yielded 1123 articles. After two levels of screening, 24 articles were identified for analysis. This included 16 case series, seven cohort studies, and one randomized controlled study. From these studies, 1235 patients and 2235 breasts were analyzed. The majority of articles reported on a change in the nipple to inframammary fold and sternal notch to nipple distances. Conclusions: In the analytical studies, superior and superomedial pedicles tended to provide greater long-term stability than inferior pedicles. Mesh, dermal suspension flaps, and muscular slings showed promise in providing additional support over standard techniques. No single procedure is ideal for all patients; however, this systematic review provides a valuable description of techniques and long-term outcomes to guide surgical planning.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".