Clinical efficacy of mechanically activated tissue retractor combined with vacuum sealing drainage for treating deep soft tissue defects: a prospective study
Bibliographic record
Abstract
Objective: This study aimed to compare the clinical efficacy of mechanically activated tissue retractor (MATR) combined with vacuum sealing drainage (VSD) versus conventional VSD for treating deep soft tissue defects.Methods: This prospective study included 53 patients with deep soft tissue defects treated between July 2024 and April 2025.The combination group (26 patients) received MATR combined with VSD, while the control group (27 patients) received conventional VSD.Outcome measures included defect healing time, rate of defect healing, mature granulation, graft survival status, pain (Visual Analog Scale, VAS), functional mobility (Activities of Daily Living Scale, ADLS), scarring (Vancouver Scar Scale, VSS), and perioperative complications.Chi-square test, t-test, and ANOVA were used to compare differences.Results: The combination group demonstrated a significantly shorter defect healing time and lower perioperative complication rate than the control group (all P < 0.05).At 14 days and 21 days after surgery, the combination group demonstrated superior defect healing, mature granulation, and skin survival status compared to the control group (all P < 0.05).Additionally, the combination group had significantly lower VAS scores and higher ADLS scores than the control group (all P < 0.05).At 3 months after defect healing, the combination group again showed significantly lower VAS and VSS scores, and higher ADLS scores than the control group (all P < 0.05).Conclusion: MATR combined with VSD was more effective in treating deep soft tissue defects compared to conventional VSD.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".