Effect of CO2 fractional laser combined with recombinant human epidermal growth factor gel on skin barrier
Bibliographic record
Abstract
To evaluate the impact of CO2 fractional laser combined with recombinant human epidermal growth factor (rhEGF) gel on skin barrier in acne scar patients. In a retrospective analysis, we examined 105 acne scar patients admitted between July 2018 and August 2021. Of these, 51 received only CO2 fractional laser (control group), while 54 underwent a combination of CO2 fractional laser and rhEGF gel (observation group). We assessed treatment efficacy, symptom relief, skin barrier parameters, pre- and posttreatment inflammatory factors, adverse reactions, posttreatment quality of life, and patient satisfaction. The observation group exhibited a higher overall response rate, significantly shorter wound healing, scab formation, and scab detachment times. Additionally, this group showed increased stratum corneum water content, decreased pH, and transdermal water loss (TEWL), and reduced hypersensitive C-reactive protein and interleukin-6 expression posttreatment. Quality of life scores were higher, with fewer adverse reactions and greater treatment satisfaction. Combining CO2 fractional laser with rhEGF gel markedly improves acne scar treatment efficacy, enhances skin barrier function, reduces inflammation, and elevates quality of life. Its safety profile supports its broader clinical adoption.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".