A comparison of traditional anti-inflammation and anti-infection medicinal plants with current evidence from biomedical research: Results from a regional study
Bibliographic record
Abstract
BACKGROUND: In relation to pharmacognosy, an objective of many ethnobotanical studies is to identify plant species to be further investigated, for example, tested in disease models related to the ethnomedicinal application. To further warrant such testing, research evidence for medicinal applications of these plants (or of their major phytochemical constituents and metabolic derivatives) is typically analyzed in biomedical databases. METHODS: As a model of this process, the current report presents novel information regarding traditional anti-inflammation and anti-infection medicinal plant use. This information was obtained from an interview-based ethnobotanical study; and was compared with current biomedical evidence using the Medline(®) database. RESULTS: Of the 8 anti-infection plant species identified in the ethnobotanical study, 7 have related activities reported in the database; and of the 6 anti-inflammation plants, 4 have related activities in the database. CONCLUSION: Based on novel and complimentary results from the ethnobotanical and biomedical database analyses, it is suggested that some of these plants warrant additional investigation of potential anti-inflammatory or anti-infection activities in related disease models, and also additional studies in other population groups.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".