Effect of general and surface anesthesia on micro-plasma radiofrequency of hypertrophic scar: A retrospective cohort study
Bibliographic record
Abstract
Abstract Background Although micro-plasma radiofrequency (MPR) treatment has a significant effect on scars, patients require anesthesia to relieve the significant discomfort it produces. Whether anesthesia impacts efficacy is unclear. Objective To evaluate the effect of different anesthesia on MPR for hypertrophic scars. Methods A retrospective cohort study involving 101 people was conducted to investigate the effectiveness and safety of general and topical anesthetics for the treatment of MPR scars. The primary measures of outcome were the Vancouver Scar Scale (VSS) scores before the first treatment and six months after the last treatment, as well as the Visual Analogue Scale (VAS) scores on the day and the day after the final treatment. Results The differences in scar pigmentation, vascularity, and overall VSS scores were higher in the general anesthesia group than in the surface anesthesia group. Patients in the general anesthesia group had a lower pain level than those in the surface anesthesia group. After adjusting for confounding factors and propensity score matching, the outcome of VSS and VAS scores was stable. There was no statistical difference in the adverse effects and satisfaction between the two groups. Conclusion General anesthesia, as opposed to surface anesthesia, may not only ensure safety but also increase the effectiveness of MPR and lessen postoperative pain in the treatment of hypertrophic scars.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".