Botanicals With Dermatologic Properties Derived From First Nations Healing: Part 1—Trees
Bibliographic record
Abstract
INTRODUCTION: First Nations people have a long history of working with medicinal plants used to treat skin diseases. The purpose was to assess the dermatologic therapeutic potential of western red cedar, white spruce, birch, balsam poplar, and black spruce. METHODS: Based on expert recommendations, 5 trees were selected that were used in First Nations medicine for cutaneous healing and have potential and/or current application to dermatology today. We searched several databases up to June 12, 2014. RESULTS: Western red cedar's known active principal compound, β-thujaplicin, has been studied in atopic dermatitis. White spruce's known active principal compound, 7-hydroxymatairesinol, has anti-inflammatory activity, while phase II clinical trials have been completed on a birch bark emulsion for the treatment of actinic keratoses, epidermolysis bullosa, and the healing of split thickness graft donor sites. Balsam poplar has been used clinically as an anti-aging remedy. Black spruce bark contains higher amounts of the anti-oxidant trans-resveratrol than red wine. DISCUSSION: North American traditional medicine has identified important botanical agents that are potentially relevant to both cosmetic and medical dermatology. This study is limited by the lack of good quality evidence contributing to the review. The article is limited to 5 trees, a fraction of those used by First Nations with dermatological properties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.006 | 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".