Efficacy and Safety of Trichloroacetic Acid in Melasma Treatment: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background: Melasma is a common acquired hyperpigmentation disorder that poses significant therapeutic challenges, particularly in darker skin types. Chemical peels, including trichloroacetic acid (TCA), are widely used as second-line treatments. However, the efficacy and safety of TCA in melasma treatment remain inconsistent across studies. This systematic review and meta-analysis aim to evaluate the efficacy and safety of TCA in melasma treatment, assessing its impact on pigmentation reduction and associated adverse effects. Methodology: A systematic search was conducted in PubMed, Embase, Scopus and Cochrane Library for randomised controlled trials (RCTs) and observational studies published up to February 2025. Studies evaluating TCA peels on melasma using validated pigmentation assessment tools, such as the Melasma Area and Severity Index (MASI), were included. Data extraction and risk-of-bias assessment were performed using the Cochrane Collaboration’s tool for RCTs and the Newcastle–Ottawa Scale for observational studies. A meta-analysis was conducted using a random-effects model to calculate pooled mean differences in MASI scores before and after treatment. Results: A total of 34 studies comprising 2166 participants were included. The pooled analysis demonstrated a non-significant reduction in MASI scores following TCA treatment (mean difference: 1.060, 95% confidence interval: −0.346–2.465, P = 0.139). Studies using 20%−35% TCA showed superior pigment reduction. Conclusion: TCA has been widely studied for melasma, but its effectiveness remains uncertain. While some studies suggested a trend towards improvement, the evidence remains inconclusive. Its safety profile warrants caution in darker skin types due to the risk of post-inflammatory hyperpigmentation.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".