Evaluating efficacy and safety of the topical silicone gel containing onion extract in the treatment of post‐cesarean surgical scars
Bibliographic record
Abstract
BACKGROUND: Cesarean section scars are post-surgical problems in women. Many active ingredients have been found to diminish scar formation. Clinical investigations on the onion extract have gained more attention due to its properties, such as improvement of scar appearance and texture. However, published studies evaluating the usefulness of the onion extract in the treatment of scars are controversial. METHODS: The three-month study period followed a prospective, randomized, and double-blinded design. Each enrolled subject's post-cesarean completely sealed wounds were divided into two halves along the closure axis. Each half was randomly assigned to the treatment with either silicone gel containing 5% onion extract or the silicone gel containing vitamin C. All subjects were respectively evaluated at the one, two, and three months of the treatment. RESULTS: After the three-month follow-up, there was a statistically significant difference in scar improvement between before and after treatment. None of statistically significant difference in the Patient and Observer Scar Assessment Scale (POSAS) and Vancouver Scar Scale (VSS) scores and melanin value was found between silicone gel containing 5% onion extract and the control silicone gel. However, the improvement of scar erythema by treatment with the silicone gel containing 5% onion extract was significantly greater than in the control group. No adverse effects were reported in either group.
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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 | 0.000 |
| 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".