Traditional knowledge of Snakebite treatment: Medicinal plants utilized by Vaidyas in Parassala, Thiruvananthapuram
Bibliographic record
Abstract
Snakebite envenomation remains a significant public health concern in rural regions of India, particularly in Kerala, where agricultural activities and human snake interactions are frequent. Despite the availability of modern antivenom therapy, traditional treatment practices by Vaidyas (indigenous healers) continue to play a vital role in primary healthcare, especially in remote areas. This ethnobotanical study documents and analyses the medicinal plants employed by Vaidyas in Parassala, Thiruvananthapuram, for the treatment of snakebites. Data were collected through semistructured interviews and field observations, recording plant species, local names, plant parts used, preparation methods, and modes of administration. A total of 15 medicinal plant species belonging to 12 families were identified, with leaves being the most commonly used plant part, followed by roots and bark. The remedies involved both oral administration and topical applications, often accompanied by specific rituals. The findings highlight the rich repository of traditional knowledge in Parassala and underline the need for conservation, scientific validation, and sustainable use of these plant resources. This study serves as a baseline for future pharmacological investigations and the preservation of cultural heritage associated with snakebite management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".