Comparative Efficacy and Safety of Injectable Tranexamic Acid Combination Therapies for Melasma: A Network Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Melasma is a challenging hyperpigmentation disorder affecting predominantly women with darker skin tones. Despite various treatments, achieving consistent and effective results remains difficult. Tranexamic acid (TXA) has emerged as a promising therapeutic agent, particularly through intradermal injections, but its optimal concentration, delivery method, and combination therapies remain unclear. In this study, the authors aim to evaluate the efficacy and safety of injectable TXA, focusing on its use alone and in combination with other treatments, such as hydroquinone, for melasma. A network meta-analysis of 9 randomized controlled trials was conducted, involving 358 participants. Data on melasma subtype, TXA concentration, delivery method, and treatment outcomes (Melasma Area and Severity Index [MASI] scores) were extracted. Statistical analysis was performed using a random-effects model, assessing both direct and indirect comparisons. The combination of TXA and 4% hydroquinone showed the most significant improvement in MASI scores compared with other interventions, demonstrating superior efficacy. Adverse effects were mild and transient, including injection-site pain and erythema, underscoring a favorable safety profile. Injectable TXA, especially in combination with hydroquinone, is an effective treatment for melasma. Standardized protocols and long-term studies are needed to optimize its use. Level of Evidence: 3 (Therapeutic).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".