Intralesional Triamcinolone Alone Vs. Combined Platelet-Rich Plasma for Keloid Treatment
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of intralesional triamcinolone acetonide alone versus its combination with platelet-rich plasma (PRP) in the treatment of keloids. STUDY DESIGN: Quasi-experimental study. Place and Duration of the Study: Department of Dermatology, MTI-Hayatabad Medical Complex, Peshawar, Pakistan, from March to September 2022. METHODOLOGY: Sixty patients with refractory keloids were enrolled and randomly assigned to two equal groups. Group A received intralesional triamcinolone acetonide (20 mg/mL) injections every three weeks for a total of four sessions. Group B received the same regimen of triamcinolone, supplemented with autologous PRP injections administered one week after each corticosteroid session. PRP was prepared using a two-step centrifugation technique and activated with calcium chloride before intralesional injection. Treatment response was assessed using the Vancouver scar scale (VSS) at 12 weeks. Data were analysed using SPSS version 23.0, with a p-value ≤0.05 considered statistically significant. RESULTS: Significant clinical improvement (≥50% VSS reduction) was observed in 86.7% of patients in Group B compared to 33.3% in Group A (p <0.001). CONCLUSION: Combining intralesional triamcinolone acetonide with PRP is significantly more effective than corticosteroid monotherapy in the treatment of keloids. KEY WORDS: Keloids, Intralesional triamcinolone acetonide, Platelet-rich plasma, Scar therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".