The Efficacy of Onion Extract on the Prevention or Treatment of Scars: A Systematic Review
Bibliographic record
Abstract
Scars are common and debilitating outcomes of burn injury, with no current consensus regarding the gold standard in scar management. Noninvasive interventions such as silicone gels are popular adjuvant treatments due to ease of application. Onion extract (OE) has been proposed as a potential scar treatment modality due to its antimicrobial and anti-inflammatory properties. A systematic search of the literature was conducted using PubMed, Scopus, and Cochrane for articles published between January 2000 and December 2021. Inclusion criteria were studies (1) involved OE gel or OE treatment and (2) those assessing scar prevention or treatment outcomes. Patient and physician reported scar outcomes after treatment and adverse effects were recorded. A total of 21 articles were included in the final review. Five studies found statistically significant improvements in overall scores and individual Vancouver Scar Scale components in the OE treatment group compared to the silicone groups. Several studies found that combined treatment of OE with other topical treatment modalities such as triamcinolone or silicone gel produced significant improvements in scar symptoms. In this review, reported adverse effects were minimal, often consisting of self-resolving pruritus, irritation, and erythema. This review supports OE's potential utility in scar prevention and treatment. Most studies reported minimal adverse events with OE application and significant benefits in specific scar characteristics. Further research is needed to investigate scar outcomes after treatment with OE with larger sample sizes and a follow-up period greater than a year.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".