Efficacy of Autologous Platelet Concentrates as Adjuvant Therapy to Surgical Excision in the Treatment of Keloid Scars Refractory to Conventional Treatments
Bibliographic record
Abstract
BACKGROUND: After conventional treatments, keloid scars show varying degrees of recurrence. The aim of this study was to assess the efficacy and safety of platelet-rich plasma in the treatment of postoperative keloid scars refractory to conventional treatments. METHODS: This pilot prospective study was conducted in 17 patients with keloid scars who did not respond to 4 injections of cortisone or radiotherapy after extralesional resection of keloid. Platelet-rich plasma was injected intraoperatively and then 3 times with a 1-month interval. The primary end point was the complete remission of keloid scars 2 years posttreatment. Scar pruritus severity was scored before and after treatment. The study protocol was approved by the ethics committee and authorized by the French National Agency. This trial was registered at ClinicalTrials.gov, identifier NCT02922972. RESULTS: Nine keloid scars (53%) were completely resolved at 2 years, and 5 (29%) completely relapsed after treatment. Pruritus severity score was significantly lower at 2 years compared with baseline (1.33 ± 0.97 before treatment and 0.40 ± 0.63 at 2 years, P < 0.003). The mean Vancouver Scar Scale score significantly improved (8.18 ± 2.38 before treatment and 3.82 ± 1.98 at 2 years, P < 0.001). CONCLUSIONS: Injecting platelet-rich plasma is an effective and safe method as adjunctive therapy to resection for treating keloid scars refractory to conventional 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".