Application of Modified Skin Stretching for Soft Tissue Defect Reconstruction in the Ankle and Foot: A Retrospective Report
Bibliographic record
Abstract
OBJECTIVE: The failure rate of foot and ankle soft tissue defect reconstruction with flap is relatively high, often posing a significant burden on patients. The aim of this study is to explore the effectiveness of repeated stretch sutures in repairing skin and soft tissue defects of the ankle and foot. METHODS: Twenty-three patients with ankle and foot skin and soft tissue defects were retrospectively analyzed between February 2016 and February 2019. Sutures were repeatedly stretched every 3-5 days. Local skin grafting was performed if necessary after wound surfaces disappeared or exposed tendons and bones were covered by soft tissue. Wound healing time, postoperative healing area, Vancouver Scar Assessment Scale, sensation, and function of the new skin were evaluated. RESULTS: Healing time was 17-35 (24.43 ± 5.29) days. Ten patients wholly healed, and 13 healed by approximately 70.08% ± 6.59%. The Vancouver Scar Assessment Scale average score was 2.83 ± 1.19 points, of which 15 cases were excellent (0-3 points) and 8 cases were good (4-7 points). The sensation and function of the new skin after repair were equivalent to those of normal skin after the last follow-up. CONCLUSIONS: Applying repeated tension sutures on the skin and soft defects of the ankle and foot reduced the skin graft area and decreased complex high-risk surgical flaps' use and transplantation area.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".