Evaluation of safety and efficacy of fractional CO2 laser in treatment of post traumatic atrophic scars
Bibliographic record
Abstract
Aim: This research evaluated the safety and efficacy of fractional CO2 LASER (FCOL) in treatment of post traumatic atrophic scarring. Research design: This was a prospective, randomized research.Place and duration of the research: Outpatient Dermatology and Venereology Clinic and Plastic Surgery Departments at Tanta University Hospitals, from December 2019 to June 2021.Methodology: This research was carried out on 20 participants with post traumatic atrophic scarring treated with FCOL. Follow up 3 months after treatment and evaluation of the improvement was done by Vancouver scar scale, 3 blinded dermatologists’ assessment, and patient satisfaction score. Results: There was improvement in all participants with variable degrees, there were no scarring showed excellent improvement, 4 scarring showed good improvement (20%), 10 scarring showed fair improvement (50%) and 6 scarring showed poor improvement (30%). Adverse reactions were in general mild and well tolerated, in the form of transient redness, mild hyperpigmentation, mild pain, that all resolved within few days. No relation between the degree of improvement and age of the participants, site of the scarring, scar duration, but in sex of the participants, it was more in males than in females. Conclusion: FCOL may be an effective approach for treating atrophic scarring, from both an aesthetic and a functional perspective.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".