Intraoperative Platelet-Rich Plasma (PRP) for Post-Cesarean Scar Healing: A Single-Center Randomized Controlled Pilot Study
Bibliographic record
Abstract
Background: Cesarean section (CS) frequently results in abdominal scarring, affecting recovery, aesthetics, and quality of life. Platelet-rich plasma (PRP), an autologous concentrate rich in growth factors, may enhance wound healing. This pilot trial assessed the effect of intraoperative PRP on CS scar outcomes. Methods: In this single-center, single-blind randomized controlled trial (February 2023–December 2024), 100 women undergoing elective CS were randomized to PRP treatment (n = 50) or standard care (n = 50). PRP, prepared from 20 mL autologous blood, was infiltrated into uterine incision margins and subcutaneously before skin closure. Scar healing was evaluated at day 7 and day 40 postpartum using the Patient and Observer Scar Assessment Scale (POSAS; physician and patient), Vancouver Scar Scale, Manchester Scar Scale, REEDA (Redness, Edema, Ecchymosis, Discharge, Approximation) Scale, Visual Analog Scale (VAS), and Numeric Rating Scale (NRS). Mann–Whitney U tests and Cohen’s d effect sizes were calculated. Results: Follow-up was complete for all participants. On day 7, PRP-treated patients had lower mean scores across most scales (e.g., Vancouver: 1.74 ± 1.58 vs. 2.54 ± 2.30; p = 0.063). At day 40, improvements persisted, with POSAS Patient scores significantly lower in the PRP group (7.24 ± 1.81 vs. 8.00 ± 2.06; p = 0.029). Effect sizes were small-to-moderate (<0.5), suggesting underpowering. No adverse events occurred. Conclusions: PRP administration during CS showed favorable trends toward improved scar quality and reduced patient-reported discomfort, with statistical significance for POSAS Patient scores at 40 days. Larger, multicenter trials with extended follow-up are needed to confirm these findings.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".