Εffects of vitamin C, silicone gel, and their combination on small- and medium-sized dermatological trauma healing: a prospective study
Bibliographic record
Abstract
Background: The process of healing a dermatologic surgical wound is highly complex. Any disruption at any stage may lead to delayed closure, prolonged hospitalization, increased expenses, and adverse effects on patients' daily routines. Aim: The primary objective of this clinical study was to assess the efficacy of postoperative interventions involving oral vitamin C or topical silicone gel, either independently or in combination, in the context of healing from dermatologic surgery trauma. Furthermore, the study sought to investigate the potential impact of inflammatory markers, such as IL-6 and CRP, as well as lifestyle interventions, specifically physical activity and smoking, on the healing process. Methodology: A prospective study was conducted from August 2017 to December 2021 at the Outpatient Dermatological Clinic in Tzaneio General Hospital in Greece to evaluate the effect of vitamin C, silicone gel, or the combination of both on small and medium-sized dermatological trauma healing. A total of 112 patients were included in the study; 29 patients had a natural postoperative healing, while 83 received oral vitamin C or topical silicone gel, or the combination of both. Scar Assessment Score and the Vancouver Scar Scale were used to evaluate the healing. Results: Patients treated with the combination of oral vitamin C and topical silicone gel experienced accelerated wound healing compared to the other treatment groups. Conclusion: Based on the research findings, it is concluded that the use of combined therapy involving oral administration of vitamin C and topical application of silicone gel accelerates the postoperative healing process in cases of dermatologic surgery trauma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".