Phyto-Extracts and Silicone Gel (JUMI) in Reduction of Surgical Scar Post-joint Arthroplasty. A Randomized Control Trial
Bibliographic record
Abstract
Background and Objective: To assess the clinical potency of JUMI Anti-Scar Cream (JASC) in the prevention of excessive scar formation post total hip replacement (THR) and total knee replacement (TKR). Patients and Methods: Our study was an open-label, prospective, randomized control to test the efficacy of JASC. After surgery, the same material was used in both groups to close the skin. After 14 days, once the staples were removed, JASC was applied twice a day for 60 days. Patients were seen at 6 and 12 weeks. At 3-month follow-ups, the scars were assessed using the Visual Analogue Scale (VAS) and Vancouver Scar Scale (VSS). Results: Forty patients were part of this trial; two patients in the control group were lost during follow-up. The average age-matched in both groups. There were no adverse or untoward effects due to the use of JASC. At 3 months, there were significant differences between the parameters assessed; VAS scores were P<0.0001. VSS parameters such as vascularity, pigmentation, pliability, and scar height were much lower compared to patients who had standard treatment (P<0.0001). Conclusion: Our study demonstrated that JASC was effective and successful in suppressing excessive scar formation. We believe that JASC has the potential as a prime anti-scar therapy, and we recommend more multicentric studies. Clinical Trial Rg. No: This trial was registered with the Sri Lankan Trial Registry vide #2022/021.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".