Clinical Observation of Microplasma Radiofrequency Technology Combined With Glucocorticoid Injection in the Treatment of Hundreds of Cases of Hypertrophic Scar After Early Deep Burn and Scald
Bibliographic record
Abstract
BACKGROUND: To investigate the clinical efficacy and safety of microplasma radiofrequency technology combined with glucocorticoid injection in the treatment of hypertrophic scarring after early deep burns and scalding. METHODS: A total of 150 patients with hypertrophic scars after early deep burns from June 2018 to June 2021 were randomly divided into 3 groups, with 50 cases in each group. The patients were treated with compound betamethasone injection (Group A), microplasma radiofrequency technique (Group B), and compound betamethasone injection combined with microplasma radiofrequency technology (Group C). Each course of treatment included 5 standard treatments, and they were performed 6 weeks apart. Each patient was analyzed using the Vancouver scar scale and visual analogy scale after each treatment. The results were compared over time and across groups using repeated measurement analysis of variance. RESULTS: A total of 138 patients in these 3 groups completed this study. As treatment continued, the Vancouver scar scale value of Group C decreased more rapidly than that of Group A and Group B, and the difference was statistically significant ( P <0.05). In the improvement of scar pain and itching, there was little difference between Group C and Group A ( P >0.05), but both were better than Group B, and the difference was statistically significant ( P <0.05). Regarding the incidence of adverse reactions, there was little difference between Group C and Group B ( P >0.05), but the incidence of adverse reactions was lower than that of Group A, and the difference was statistically significant ( P <0.05). CONCLUSION: Microplasma radiofrequency combined with glucocorticoid injection in the treatment of hypertrophic scarring after early deep burns is effective, safe, and has a low incidence of adverse reactions, and it merits clinical promotion.
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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".