Clinical observation of continuous skin distraction technique combined with vacuum sealing drainage in the treatment of deep wounds
Bibliographic record
Abstract
Objective To investigate the clinical effect of continuous skin distraction technique combined with vacuum sealing drainage (VSD) on the treatment of deep wounds. Methods From May 2015 to May 2018, 20 patients with deep wound in different parts were selected from the Department of Emergency Medicine, Shanxi Bethune hospital, including 10 cases of trauma, 7 cases of pressure sore and 3 cases of infection wound. According to the situation of wounds, continuous skin distraction combined with VSD was applied after debridement. The wound healing time was observed and recorded. The Vancouver scar scale of all the patients was used to evaluate the wound healing quality. Results The wounds of 20 patients were healed well after continuous stretch of skin and VSD treatment. The average healing time of 5 patients with wound size of 2 cm×2 cm to 3 cm×4 cm was 20 days, the average healing time of 8 patients with wound size of 5 cm×5 cm to 8 cm×10 cm was 22 days, and the average healing time of 7 patients with wounds ranging from 10 cm×15 cm to 15 cm×20 cm was 30 days. The average vancouver scar scale score of 3 months after wound healing was 3.8 point. Conclusion The application of continuous skin distraction combined with VSD technology to treat deep wounds not only can obtain satisfactory clinical results, but also has the advantages of simple operation and small trauma, which is worth promoting. Key words: Negative-Pressure Wound Therapy; Wound Healing; Drainage; Skin-stretching devices
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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 | 0.000 | 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".