EKSTRAK LIDAH BUAYA TOPIKAL MEMBERIKAN PENGARUH PADA KONSISTENSI SKAR HIPERTROFIK PASKA LUKA BAKAR
Bibliographic record
Abstract
Introduction: Post-traumatic burns that already heal, very often happens that an ugly scar in the form of hypertrophic scarring, keloids and are often accompanied by the presence of contractures. Hypertrophic and keloid scar is fibroproliferation abnormalities in the dermis that is unique to humans that may arise as a result of burns. While contractures occur because of the shortening of the pathological scarring that causes deformities and movement disorders such as the limited movement of a body part. cases of burns caused 195,000 deaths/year worldwide, especially in poor and developing countries. This aim of this study was to analyze the effect of topical aloe extract against consistency, height, and pigmentation scarring after burns hypertrophic Poly Plastic Surgeon Dr. Soetomo. Methods: This research was used in the pre-experimental design. The population was all patients with burns and 10 patient samples. It used consecutive sampling. The independent variable was aloe vera topical and the dependent variable was the quality of the hypertrophic scar. Data were collected using the Vancouver Scar Scale and then analyzed using Wilcoxon Signed Ranks and Paired T-Test. Result: The results showed that there was significant effect before and after basting of topical aloe vera on the value of consistency (p=0.005). No significant effect before and after the aloe vera in the pigmentation scores (p=0.317) and height. There was a difference significant hypertrophic scar consistency before and after aloe vera extract. No differences in height and pigmentation significant hypertrophic scars before and after extract of aloe vera extract topical effect on the consistency, but not in height and pigmentation in hypertrophic scarring after burns. Conclusions: Further studies with more samples to determine the substances contained in aloe vera that can accelerate changes in consistency, pigmentation, and height.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".