Botanicals With Dermatologic Properties Derived From First Nations Healing: Part 2—Plants and Algae
Bibliographic record
Abstract
INTRODUCTION: Plants and algae have played a central role in the treatment of skin conditions in both traditional First Nations healing and in modern dermatology. The objective of this study was to examine the evidence supporting the dermatological use of seaweed, witch hazel, bearberry, and mayapple. METHODS: Four plants and algae used in traditional First Nations treatments of skin disease were selected based on expert recommendations. Several databases were searched to identify relevant citations without language restrictions. RESULTS: Seaweed has potential clinical use in the treatment of acne and wrinkles and may be incorporated into biofunctional textiles. Witch hazel is an effective and well-tolerated treatment of inflammation and diaper dermatitis. Bearberry leaves contain arbutin, a skin-lightening agent that is an alternative for the treatment of hyperpigmentation. Mayapple contains podophyllotoxin, a treatment for condyloma accuminata, molluscum contagiosum, and recalcitrant palmoplantar warts. DISCUSSION: Common plants and algae are replete with bioactive agents that may have beneficial effects on the skin. Further research will open the door to new and innovative products in the future. Limitations of this study include that the scope of our study is limited to 4 plants and algae, a small sample of the breadth of plants used by First Nations for dermatological treatments.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".