Efficacy of IPL device combined with intralesional corticosteroid injection for the treatment of keloids and hypertrophic scars with regards to the recovery of skin barrier function: A pilot study
Bibliographic record
Abstract
Background: Keloids and hypertrophic scars are prevalent and psychologically distressful dermatologic conditions. Various treatment modalities have been tried but without complete success by any one method. Objective: We evaluated the efficacy of a combination of intense pulsed light (IPL) device and intralesional corticosteroid injection for the treatment of keloids and hypertrophic scars with respect to the recovery of skin barrier function. Methods: Totally 52 Korean patients were treated by the combined treatment at 4–8-week intervals. Using digital photographs, changes in scar appearance were assessed with modified Vancouver Scar Scale (MVSS), physicians’ global assessment (PGA) and patient's satisfaction score. In 12 patients, the stratum corneum (SC) barrier function was assessed by measuring transepidermal water loss (TEWL) and SC capacitance. Results: Most scars demonstrated significant clinical improvement in MVSS, PGA and patient's satisfaction score after the combined therapy. A significant decrease of TEWL and elevation of SC capacitance were also documented after the treatment. Conclusion: The combination therapy (IPL + corticosteroid injection) not only improves the appearance of keloids and hypertrophic scars but also increases the recovery level of skin hydration status in terms of the skin barrier function.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".