Effectiveness of Dermapen Action Using Green Belt Leaf Extract Cream (Piper betle L) On Hair Growth on The Skin Surface of Female Wistar Strain Rats (Rattus Norvegicus)
Bibliographic record
Abstract
Hair loss is a very prevalent hair issue. Hair loss is typical for all humans, but if it becomes severe, it can lead to baldness. Hair goes through a unique cycle of development and loss for each strand, and one herbal plant that has the activity of fertilizing hair growth and overcoming the problem of hair loss is green betel leaf (Piper betle L). The purpose of this study was to determine the efficacy of a derma pen utilizing green betel leaf extract cream (Piper betle L) on hair development on the skin surface of female Wistar rats (Rattus norvegicus). Betel leaves have been shown to help with issues such as hair loss. Using betel leaves regularly aids in It promotes hair growth, conditions hair, and makes it thick and long. Betel leaves can also assist with itching, dandruff, and broken ends. The polyphenol and flavonoid content in betel leaves is an antioxidant and anti-inflammatory, protecting hair from damage caused by inflammatory skin illnesses and free radicals that cause hair loss on the head.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".