Intralesional 5-Fluorouracil for Keloids: A Systematic Review
Bibliographic record
Abstract
Keloids are benign, fibroproliferative dermal tumours, often arising after trauma, that are more common in darker skin types. Numerous therapeutic options have been employed for the treatment of keloids; however, there is no one gold standard approach. Five-fluorouracil, a potent chemotherapeutic agent, has emerged as a promising therapeutic option. Therefore, this systematic review, using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focused on providing a broad overview of the use of 5-fluorouracil for the management of keloids. Forty studies (2325 patients) met inclusion criteria and investigated 5-fluorouracil for keloid management, with 19 studies (1043 patients) including a 5-fluorouracil monotherapy group. Five-fluorouracil monotherapy demonstrated consistent keloid improvement with >254 keloids injected across various anatomical regions. Five-fluorouracil monotherapy was most often compared to intralesional triamcinolone acetonide, utilizing the Patient and Observer Scar Assessment Scale and the Vancouver Scar Scale. The most common keloid parameters assessed were height, size, volume, width, length, induration, pruritus, and erythema. Five-fluorouracil monotherapy exhibited substantial improvements, with weight averages of 73% of patients experiencing >25% improvement and 67% achieving >50% improvement. Relapse rate was 16% at 27 weeks after 5-fluorouracil monotherapy treatment. Limitations included potential selection bias, language restrictions, and heterogenous data analysis among studies. Overall, our findings underscore the potential effectiveness of 5-fluorouracil monotherapy in the management of keloids, with an encouraging safety profile. Larger prospective trials are needed to determine optimal therapy or combination therapy for the management of keloids. This detailed compilation of treatment protocols, outcomes, and relapse rates stand as a valuable resource for further research and clinical applications.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| 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".