Keloid Treatment Using Plasma Exeresis: A Pilot Trial Study
Bibliographic record
Abstract
Introduction: Keloid scars and hypertrophic scars are more commonly seen after surgeries, suture placements, or other skin damages. Scars can be treated using a variety of methods, including topical compounds, surgery, and lasers. The aim of this study is to evaluate the effects of plasma exeresis on the treatment of keloid scars. Methods: This experimental study was conducted on patients with keloid scars, defined as a treatment-resistant subtype of scars with extension beyond the primary skin defect and cauliflower appearance, in different parts of the body. The patients were treated with 2-to-3-session plasma exeresis. Scars were examined based on the Vancouver scar scale (VSS) before and 5 months after the treatment. Results: A total number of 24 scars were enrolled in this study. The number of patients was 16. There was a decrease in the mean thickness of keloids from 2.20 to 0.54 (P=0.000). The mean pigmentation and pliability scores decreased from 1.54 and 2.16 to 0.375 and 0.541, respectively (P=0.001, 000). There was a significant reduction in the keloid scar vascularity score from 1.666 to 0.541 (P=0.000). There was a decrease from 0.708 to 0.00 (P=0.004) in the mean itchiness score. After the intervention, the mean pain score was 0.000, compared to 0.7500 before the intervention (P=0.003). There was a decrease in the total score from 8.958 to 2.000 (P=0.000). Conclusion: The plasma exeresis procedure is effective in destroying small keloid scars. Furthermore, results in less itching and pain, as well as no significant complications or recurrences.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".