Review of ethnobotanical studies on medicinal plants that used to treat diarrhea and dysentery in Ethiopia
Bibliographic record
Abstract
Background: Diarrheal diseases are a major public health problem. Studies indicated that medicinal plants are being lost as a result of combined effects of environmental degradation, agricultural expansion, and deforestation and over harvesting of species. Poor resource administration, lack of awareness on herbal medicine and oral transmission of knowledge from generation to generation have also a great contribution for the lost. As a result, this review was initiated to document the medicinal plants, particularly those plants that used to treat diarrhea or/and dysentery in Ethiopia. Therefore, the data could be used as a basis for further studies.Objective: To review medicinal plants that used to treat diarrhea or/and dysentery and to document it that could be used as a source of knowledge to scientists.Methods: A review of available ethnobotanical literature on Ethiopian traditional medicine particularly that was related to diarrhea treatment was carried out. Google scholar online internet searches were done.Result: Ninety (90) medicinal plants distributed in 51 families were found from the reviewed studies. Of the 51 families 19 consist at least 2 species within each family, where as the remaining consist only one species. Most of them are used for diarrhea (76.5%), dysentery (16.5%) and the remaining 7 % used for both diarrhea and dysentery. Some of the plants also used for stomachache, vomiting and intestinal parasite therapy. Of different plant parts leaves are the most commonly used followed by roots, fruits, seeds and barks that used by preparing in different methods.Conclusion: This review has identified a number of important medicinal plants used by the Ethiopian community and traditional healers for the treatment of diarrhea/dysentery which provides a baseline for future phytochemical and pharmacological investigations. It has also identified the presence of bad practices that need improvement for better service such as absence of accurate and reproducible doses that need for a multidisciplinary approach to develop potentially effective dose.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".