Adipose Tissue Transfer in Dynamic Definition Liposculpture—PART I. Back: Latissimus Dorsi and Trapezius Muscles
Bibliographic record
Abstract
The aesthetics for the male posterior torso remain a topic not fully studied in body contouring surgery, neither the lipoinjection of its muscles have been considered before. As a result, we carried out a retrospective cohort study including patients who underwent fat grafting of either the trapezius or the latissimus dorsi muscles as part of dynamic definition liposculpture (HD2). Methods: We performed cadaveric dissections to support the fat grafting technique for both the trapezius and the latissimus dorsi muscles. We also searched our records for patients who underwent fat grafting of these muscles in addition to HD2 from January 2016 to November 2021 at a single center in Bogotá, Colombia. Results: Thirty-five consecutive patients met the inclusion criteria. In total, 22 (63%) and 7 (20%) of 35 underwent fat grafting at the trapezius and the latissimus dorsi muscles alone, respectively, and 6 out of 35 (17%) of both. Mean age is 39 years (range = 22-63). All patients were men. No complications were recorded related to fat grafting. Almost all patients were satisfied with the procedure (97%). Follow-up period ranged from 2 to 48 months. Conclusions: Liposuction might not be enough to achieve the ideal V-shape of the men's back in some cases; hence, fat grafting of the power muscles becomes the best option. Recognition of the main neurovascular pedicle, proper preoperative markings, and a correct surgical technique ensure both the safety and the reproducibility of the technique.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".