The Effect of Stromal Vascular Fraction on Scar Formation of Transverse Rectus Abdominis Muscle Flap Donor Sites: A Pilot Study
Bibliographic record
Abstract
Background: Stromal vascular fraction (SVF), which plays a substantial role in wound healing, has been discussed in many recent studies concerning its positive effect on scar formation. Our study explored the effects of SVF on scar formation from partially-removed transverse rectus abdominis muscle (TRAM) flaps after inset.Methods: From December 2017 to May 2020, we enrolled 11 patients undergoing breast reconstruction performed by a single surgeon using the free TRAM flap. As a split-body, placebo-controlled study, SVF was subcutaneously injected into one side of the abdomen, and normal saline was injected into the other side of each patient. Scarring was evaluated using the Vancouver Scar Scale (VSS) and Patient Scar Assessment Questionnaire (PSAQ) at 1, 6, and 12 months after surgery, and histology was evaluated with immunofluorescence analysis at 6 months after surgery.Results: No statistically significant differences were noted in the total scores or subcategory score of the VSS and PSAQ between the test and control groups. Some patients showed more positive staining for alpha smooth muscle actin, collagen type I, and type III in the test group than in the control group. However, quantification of positively stained areas showed no statistically significant difference.Conclusion: Intraoperative SVF injection had no demonstrable clinical effect on scar quality. Histology with immunofluorescence analysis also failed to demonstrate any significant effect of SVF on scars at the microscopic level. Despite previous studies indicating the positive effects of SVF on scar quality, this pilot study questions its true effectiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".