[Therapeutic effect of mucopolysaccharide polysulfate cream in prevention of postoperative scars].
Bibliographic record
Abstract
OBJECTIVE: To assess the therapeutic effect of mucopolysaccharide polysulfate cream in prevention of postoperative scars. Methods: One hundred postoperative patients were divided into an experimental group and a control group (each n=50). After stitch removal, the experimental group wiped mucopolysaccharide polysulfate cream, and the control group wiped urea cream. The scars of the two groups were evaluated by Vancouver Scar Scale (VSS) on the day of stitch removal and in the 4th, 8th, 12th, 16th, 20th, 24th, 28th, and 32th weeks during the treat process. Results: At the beginning of treatment, the total VSS scores in the experimental group were always lower than that in the control group (P<0.05). There was no difference between the two groups in the color scores at each time point of follow-up (P>0.05). Form the 20th week, the vascular distribution scores in the experimental group were lower than that in the control group (P<0.05). And the thickness and flexibility scores in the experimental group were lower at each time point of follow-up (P<0.05). There were no differences between the two groups in wounds in head, face, or neck in the total VSS scores and all index scores (P>0.05), and the total VSS scores in the experimental group, who had wounds in chest, shoulder, or back, or had wounds in waist, abdomen, or hip, or had wounds in extremity, were lower than that in the control group (P<0.05). The vascular distribution and thickness scores in the experimental group, who had wounds in chest, shoulder, or back, were better than that in the control group (P<0.05). Conclusion: Wiping mucopolysaccharide polysulfate cream after operation as soon as possible can effectively prevent scar hyperplasia, and it is worth to be widely applied in clinic.
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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 | 0.000 |
| 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) | 0.002 | 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".