Adding herbal extracts to silicone gel on post-sternotomy scar: a prospective randomised double-blind study
Bibliographic record
Abstract
OBJECTIVE: Silicone gel has been shown effective in improving healing post-sternotomy scars. It remains to be determined whether adding herbal extracts to the gel would augment the healing effect. METHOD: and Paper Mulberry) and Group 2: silicone gel. Patients were treated for six months. The postoperative scars were assessed at three and six months by plastic surgeons using the Vancouver Scar Scale (VSS) and the patient assessment scar scale. RESULTS: Each group comprised 23 patients (n=46 in total). The VSS was significantly lower in Group 1 than in Group 2 (p=0.018 and p=0.051, respectively). In Group 1, the four differences from baseline were vascularity scores at three and six months (-0.391, p=0.025; -0.435, p=0.013, respectively), and pigmentation scores at three and six months (-0.391, p=0.019; -0.609, p=0.000, respectively). In Group 2, differences from baseline were the pigmentation and vascularity score at six months (-0.6609, p=0.000; -0.348, p=0.046, respectively). CONCLUSION: Our results suggest, post-sternotomy scars trend to have better vascularity and pigmentation when treated with silicone gel plus herbal extracts.
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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) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".