Snakebites in the Americas: a Neglected Problem in Public Health
Bibliographic record
Abstract
Abstract Purpose of Review We explored the current priority given to snakebites in 26 countries of the Americas. To describe the epidemiological characteristics of the snakebites in the Americas and the Caribbean, we looked at information collected from epidemiological sources, publications, and available from PubMed, SciELO, and LILACS. In the case of Honduras, some gray literature (theses and conference abstracts) was obtained through local networks. We also aimed at obtaining any reference made in those reports with regard to the most common snake species in the region and their toxin and the physical and mental disability in snakebite victims. Recent Findings Many countries do not keep official reports of the snakebite incidents. In a few countries, growing knowledge of venom toxicology is leading to research and development of new antivenoms. Additionally, interest is increasing in the identification of natural treatment for symptoms caused by snake venoms, especially inflammation, pain, and blood loss. There are opportunities to undertake rigorous examination of traditional treatments, which could be incorporated to the standard of care. Summary Snakebite surveillance needs improvement in several countries, and access to prompt treatment needs to be facilitated. With a few exceptions, scientific research is scarce in most Latin American countries. For prevention and management initiatives, it is important to highlight that the typical profile of the snakebite victim is a young male farmer with low literacy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".