The neglected burden of snakebites in Cameroon: a review of the epidemiology, management and public health challenges
Bibliographic record
Abstract
OBJECTIVES: Snakebite is an underestimated medical and surgical emergency in developing countries responsible for a high disease burden. Optimal management of snake envenomation in these resource-limited settings is precluded by several public health challenges. In this review, we discuss the disease burden of snakebites in Cameroon and the public health challenges of its management in view of making recommendations essential for policy-making. MEDLINE, African Journals Online and Google Scholar were searched from January 1990 to February 2017 for studies addressing snakebite in Cameroon. Our search extended to include grey literature from book chapters, conference proceedings, theses and documents from organizations. RESULTS: Our results suggest that snakebites pose a significant health and economic burden in Cameroon. A composite of factors contributes to the challenge of managing snakebites in Cameroon and include: inadequate disease surveillance; poor health-seeking behaviours of patients; under-production and scarcity of anti-venom serum and the relatively high cost of anti-venom serum. There is an urgent need to revamp the current health policies through health education, promotion and building of sustainable health systems. Disease surveillance and management can be improved by providing refresher courses for healthcare providers and subsidization of the prices of anti-venom serum in pharmacies in the country.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".