A comparative study of effectiveness of microneedling with and without topical corticosteroids in post-burn hypertrophic scars of face and neck
Bibliographic record
Abstract
Background: The standard treatment of post-burns scars has unsatisfactory outcomes and required several treatments. Objective: To evaluate efficacy of the microneedling with or without topical steroids on treatment of post-burn hypertrophic scars in face and neck. Patients and Methods: We included patients with post-burn hypertrophic scar of face and neck, caused by burn within the 1st year after burn, we excluded patients with coagulation defects. Patients were divided into 3 groups; Group A: microneedling once/month for 5 months, Group B: microneedling with topical steroids once/month for 5 months, and Group C: control group for just conservative treatment. Histopathological study was used for evaluation. Results: we included 60 participants; the mean age was 20 ± 9 years. After 3 & 6 months microneedling significantly decrease the Vancouver scar scale (VSS), and adding steroids significantly improve the results. Microneedling group significantly decreased the VSS after 3 and 6 months. Moreover, adding steroids significantly improved the results. Histopathologicaly, after 6 months, there was statistically difference between the three groups in thickness (p= <0.001), Nodules (p= 0.02) and inflammation (p= 0.02) of the scar. Conclusion: Microneedling with or without topical steroids found to improve the outcomes of post-burn 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 | 0.001 |
| 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) | 0.001 | 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".