Postbariatric Brachioplasty with Posteromedial Scar: Physical Model, Technical Refinements, and Clinical Outcomes
Bibliographic record
Abstract
BACKGROUND: Brachioplasty is an increasingly performed procedure following massive weight loss. A visible scar is the main hindrance to this surgery. The aims of the study were to develop a physical model to investigate the ideal location of the surgical incision and to present the authors' technical refinements with the posteromedial scar approach. METHODS: Twenty-four postbariatric patients underwent brachioplasty with posteromedial scar placement, concomitant liposuction, fascial plication, and axillary Z-plasty. Skin specimens were tested and a physical model of the arm was set up to investigate the difference in mechanical stress on the posteromedial and medial scars. The validated Patient and Observer Scar Assessment Scale, the Vancouver Scar Scale, and a questionnaire assessing subjective improvements were administered to patients. Preoperative and postoperative photographs were assessed by three independent plastic surgeons. RESULTS: The physical model showed that stress intensity and distribution along the scar were reduced in the posteromedial location, with smaller scar displacement in the loading simulations. Twenty-three patients healed uneventfully. One (4.1 percent) had a 2-cm dehiscence. Mean Patient and Observer Scar Assessment Scale scores were, respectively, 2 ± 0.76 and 2.13 ± 0.64 in the patients' and observers' questionnaires. The mean Vancouver Scar Scale value was 3.5 ± 1.7. Questionnaires assessing the subjective outcomes showed a mean value of 3.45 ± 0.63 of 4. The surgeons' assessment resulted in a score of 4.5 ± 0.4 of 5. CONCLUSIONS: The physical model demonstrated that the posteromedial scar was subjected to lower mechanical stress and displacement. The reported technical refinements allowed pleasant arm recontouring to be achieved with acceptable scarring and a low incidence of complications. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".