Current Best Evidence for 5 Promising Medications Used for Scar Minimization Therapy
Bibliographic record
Abstract
Introduction: Wound healing by fibrosis allows for closure of a wound, but leaves behind a permanent scar with physical and psychological effects. The primary aim of this narrative review was to summarize the current status of the evidence supporting the use of oral or topical medications to minimize scarring in humans. Methods: With the help of a health sciences librarian, PubMed, Embase, and Scopus were searched up to March 31, 2023, to investigate potential medications to ameliorate scarring. Based on this search, the medications pirfenidone, losartan, trichostatin A, enalapril, and atorvastatin were identified as 5 therapies with the most research to support their use. Studies discussing noncutaneous scarring (myocardial, intraabdominal, etc) or in animal models were excluded. Results: There is a paucity of quality literature describing the use of oral or topical medications to minimize fibrosis and produce more favorable scarring. Six studies describing the medications listed above all demonstrated an improvement in scarring parameters, most commonly based on the Vancouver Scar Scale. Conclusions: Though preliminary, emerging evidence suggests that therapies already exist with the potential to improve cutaneous scarring. Some of these medications are already ubiquitous, affordable and have a known safety profile. Excitingly, these treatments are either oral or topical, meaning that they are more accessible for patients than some current modalities for scar treatment, including steroid injections or laser therapy. Further larger-scale trials are needed before these treatments can be recommended as a routine part of scar management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".