Workgroup Report: Public Health Strategies for Reducing Aflatoxin Exposure in Developing Countries
Bibliographic record
Abstract
Consecutive outbreaks of acute aflatoxicosis in Kenya in 2004 and 2005 caused > 150 deaths. In response, the Centers for Disease Control and Prevention and the World Health Organization convened a workgroup of international experts and health officials in Geneva, Switzerland, in July 2005. After discussions concerning what is known about aflatoxins, the workgroup identified gaps in current knowledge about acute and chronic human health effects of aflatoxins, surveillance and food monitoring, analytic methods, and the efficacy of intervention strategies. The workgroup also identified public health strategies that could be integrated with current agricultural approaches to resolve gaps in current knowledge and ultimately reduce morbidity and mortality associated with the consumption of aflatoxin-contaminated food in the developing world. Four issues that warrant immediate attention were identified: a) quantify the human health impacts and the burden of disease due to aflatoxin exposure; b) compile an inventory, evaluate the efficacy, and disseminate results of ongoing intervention strategies; c) develop and augment the disease surveillance, food monitoring, laboratory, and public health response capacity of affected regions; and d) develop a response protocol that can be used in the event of an outbreak of acute aflatoxicosis. This report expands on the workgroup's discussions concerning aflatoxin in developing countries and summarizes the findings.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".