Expert Opinion: Efficacy of superficial chemical peels in active acne management – what can we learn from the literature today? Evidence‐based recommendations
Bibliographic record
Abstract
BACKGROUND: Superficial chemical peels offer therapeutic results in a convenient, affordable treatment. Many clinicians use these peels in the treatment of acne and acne-prone oily skin. OBJECTIVES: This article examines the evidence base that supports the widespread use of superficial peels in this setting. METHODS: A search of the English language medical literature was performed to identify clinical trials that formally evaluated the use of chemical peeling in active acne. RESULTS: Search of the literature revealed very few clinical trials of peels in acne (N=13); a majority of these trials included small numbers of patients, were not controlled and were open label. The evidence that is available does support the use of chemical peels in acne as all trials had generally favourable results despite differences in assessments, treatment regimens and patient populations. Notably, no studies of chemical peels have used an acne medication as a comparator. As not every publication specified whether or not concomitant acne medications were allowed, it is hard to evaluate clearly how many of the studies evaluated the effect of peeling alone. This may be appropriate, however, given that few clinicians would use superficial chemical peels as the sole treatment for acne except in rare instances where a patient could not tolerate other treatment modalities. CONCLUSIONS: In the future, further study is needed to determine the best use of chemical peels in this indication.
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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 | 0.021 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".