Decision-Making Algorithm for Advanced Excisional Body Contouring: Dynamic Definition Solutions for Skin Laxity
Bibliographic record
Abstract
BACKGROUND: Excisional body contour surgery is the cornerstone treatment for skin laxity. Decision-making can be challenging when selecting the procedure. Dynamic definition liposculpture allows the surgeon to carve the underlying anatomy and provide more natural results, in which umbilical shape and position play a crucial role. The authors describe their experience using a decision-making algorithm as a tool to ease surgical planning for advanced excisional body contouring. METHODS: Following the algorithm designed by the senior author regarding excisional body contouring procedures, the authors searched their database for patients who were classified according to skin laxity and navel location to undergo one of the following procedures: mixed technologies plus umbilical mobilization, mixed technologies plus sliding mini-abdominoplasty, mini-tummy tuck with muscular plication, full abdominoplasty, reverse bridge abdominoplasty, or reverse full abdominoplasty. RESULTS: A total of 563 women were consecutively operated on from February of 2014 to January of 2020. The six-procedure model algorithm helped the authors achieve very good results with low complication rates in patients with some grade of abdominal skin laxity. Most complications were reported as minor (9.6 percent). Major complications (3.9 percent) included three localized infections, four abnormal skin retractions, two cases of skin flap necrosis, and 13 cases of postoperative anemia. CONCLUSIONS: This algorithm helped the authors choose the best excisional technique based on patients' anatomical features by following skin geometry to enhance aesthetic outcomes. Further studies are needed to support the algorithm validation and aesthetic outcomes. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".