<scp>Endo‐Radiofrequency</scp> subcision for acne scars treatment: A case series study
Bibliographic record
Abstract
BACKGROUND: Acne scars have important psychosocial suffering for patients. Several interventions have been utilized to treat acne scars that have different degrees of efficacy and side effect. Multimodal method can attain better results to improving the physical appearance of the patients that can significantly increase the quality of life. Subcision is a recognized treatment procedure particularly for rolling acne scars, but it needs modification to increase the effect of procedure. AIMS: The aim of the study was to assess the efficacy and safety of Endo-Radiofrequency (Endo-RF) subcision in acne scars treatment. METHODS: In this study, 9 adult patients with atrophic acne scars were enrolled. The patients receive Endo-RF subcision one time and followed up for 6 months. Outcome was measured using biometric assessment by Visioface 1000 D, Mexameter and skin ultrasound imaging system, post-treatment photographs and patient's satisfaction. RESULTS: The results showed that patients had significant improvement from baseline according to the reduction of the number of skin fine and large pore (p < 0.05) and spots (p < 0.05). Also, the density and thickness of the dermis and epidermis were significantly increased (p < 0.05). CONCLUSIONS: Endo-RF subcision modality can consider as a safe and effective method for acne scar treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".