The clinical healing effect of vacuum sealing drainage technique using in treatment of deep burn wound
Bibliographic record
Abstract
Objective To explore the healing effect of vacuum sealing drainage (VSD) technique using in treatment of deep burn wound. Methods Patients were selected who hospitalized in our hospital because of burn from June 2014 to May 2016, and divided into the VSD group and silver sulfadiazine (SD-Ag) group. The incidence of wound infection, bacterial culture positive rate and wound visual analogue score (VAS) were compared between two groups of patients in different time after burn, and also compared two groups of patients with wound healing time, and incidence of scar and the scar rating scale of vancouver. Results A total of 80 patients were included in the study, and divided into VSD (n=46) and SD-Ag (n=34) groups. The wound infection rate and bacterial culture positive rate in the VSD group were significantly lower than the SD-Ag group after burn for 7 d, 14 d, and 21 d (P<0.05). Patient's wound pain VAS score in the VSD group was less than SD-Ag group after burn for 7 d, 14 d, and 21 d (P<0.05). The wound healing time, incidence of scar, and vancouver scar table score in the VSD group were significantly lower than that of SD-Ag group (P<0.05). Conclusions The use of vacuum sealing drainage of treatment for burn patients can significantly reduce the wound infection, relieve wound pain, promote wound healing, and reduce scar occurred at the same time. Key words: Negative-pressure wound therapy; Burns/TH; Wound healing
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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".