Efficacy of a Silicone-Based Gel Containing Pracaxi Oil (Pentaclethra macroloba) for Treating Post-Surgical Scars
Bibliographic record
Abstract
Scars are an unavoidable consequence of cutaneous surgery. Healing with an excellent cosmetic outcome is a crucial component to any surgical wound to avoid any negative impact on quality of life. Various products exist which claim to improve post-surgical scar appearance and texture. In this blinded, randomized pilot study, we compared the efficacy of a silicone-based topical gel containing Pracaxi oil (PO Gel; Serica™ Moisturizing Scar Formula; Cynova Laboratories, Houston, TX) against a second silicone-based gel containing Cepalin onion extract (OE Gel; Mederma® Advanced Scar Gel, Merz, North America). The Vancouver Scar Scale (VSS), Physician and Subject Global Assessment of Scar Treatment, and digital photography were used to determine efficacy and superior post-surgical care treatment outcomes. Forty healthy subjects (18-75 years old) with recent surgical scar (1 to 4 months old) were randomized to PO gel or OE gel and asked to apply a topical solution three times daily for 8 weeks. There were six study visits (Baseline and Weeks 2, 4, 8, 12 and 16). The results of this study showed that subjects with post-surgical scars achieved significant improvements at 8 and 12 weeks following application of a product with either Pracaxi oil or onion extract gel, based on mean Vancouver Scar Scale scores. Both products generally improved the individual scar signs and symptoms. Subjects using the onion extract product did not achieve improvement in Pain or Itch at the 8-week evaluation or Pain at the 12-week evaluation.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".