Comparative Cancer Risk Assessment to Estimate Risk of Hepatocellular Carcinoma Attributable to Dietary Exposure of Aflatoxin through a Surrogate (Maize) in Eastern Mediterranean Region (Iran) as Compared to East (Canada) and West Pacific (China) Regions
Bibliographic record
Abstract
Background:Hepatocellular Carcinoma (HCC) is the most common primary liver malignancy and the second leading cause of cancer related deaths worldwide. Chronic infection with Hepatitis B (HBV) and Hepatitis C (HCV) virus are significant risk factors of HCC. Aflatoxins, type of mycotoxin, produced by fungi Aspergillus flavus and Aspergillus parasiticus are potent liver carcinogen. This cancer risk assessment was conducted with aim to compare risk of aflatoxin attributable HCC in the countries of Eastern Mediterranean region with countries of region of Americas (East) and Western Pacific region (West). Methods:Cancer risk assessment was conducted using data on maize consumption for select countries from food balance sheets (FBS) database of the Food and Agriculture Organization of the United Nations (FAO), aflatoxin contamination level of the maize and chronic HBV infection prevalence from 37 publications retrieved from literature review. Results:Risk of HCC attributable to aflatoxin exposure in Iran is 0.02 (0.0001 – 0.095) HCC cases per 100,000 chronic HBV negative population which reflects in 15-75 HCC cases per year. Among the select WHO regions, most cases occur in China and Philippines from Western Pacific region and Mexico from region of Americas. Conclusion:Risk of aflatoxin related HCC in Iran is comparable to high income countries from region of Americas and Western Pacific region.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".