Snakebites in the Central American Region: More Government Attention Required
Bibliographic record
Abstract
A review was conducted on snakebites in Central America. Information was extracted using the databases of PubMed, SciELO, and LILACS. Information included retrospective studies, case reports, and case series; in this way, valuable information was retrieved from limited sources. The identified studies comprised those discussing envenoming snakebites. Several species were identified, but three of them had major epidemiological features impacting envenoming by snakebites: Bothrops asper, Crotalus simus, and Micrurus sp. Adolescents and young adult males living in rural areas and engaged in agricultural activities were identified as the main victims of snakebites by clinical records. Symptoms of local damage in the bite sites included edema and skin and muscle necrosis. In addition, the cardiovascular system was affected, with symptoms like hypotension, bleeding, and coagulation disorders. Neurotoxicity causing sensitivity and motricity problems was also reported. For El Salvador, accidents caused by Crotalus simus and Micrurus spp. were given more attention due to their greater relevance. The role of Bothrops species was more relevant in the envenoming reported by other countries. Treatment was found to be provided based on antivenoms produced in Costa Rica, and the recovery of the patients depended on the time elapsed between the accident and the initial treatment in the healthcare system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".