Foodborne and Waterborne Pathogenic Bacteria in Selected Organisation for Economic Cooperation and Development (OECD) Countries
Bibliographic record
Abstract
The World Ranking Food Safety Performance reports by Charlebois in 2008 and 2010 importantly stimulated international discussion and encouraged efforts to establish realistic international benchmarks for food safety performance among Organisation for Economic Cooperation and Development (OECD) countries. This paper presents the international incidence of 5 common foodborne pathogens and describes the challenges of comparing international data. Data were compiled from surveillance authorities in the countries, such as the Natl. Notifiable Diseases Surveillance System of Australia; the Canadian Notifiable Diseases Surveillance System; the European Food Safety Authority, EFSA; the Ministry of Health, Labour and Welfare of Japan; New Zealand Food Safety Authority; and the U.S. Center for Disease Control and Prevention. The highest average rates in cases per 100000 people over the 12-y period from 2000 to 2011 for Campylobacter spp. (237.47), Salmonella spp. (67.08), Yersinia spp. (12.09), Verotoxigenic/Shiga toxin producing Escherichia coli (3.38), and Listeria monocytogenes (1.06) corresponded, in order, to New Zealand, Belgium, Finland, Canada, and Denmark. Comparatively, annual average rates for these 5 pathogens showed an increase over the 12-y period in 28%, 17%, 14%, 50%, and 6% of the countries for which data were available. Salmonella spp. showed a decrease in 56% of the countries, while incidence of L. monocytogenes was constant in most countries (94%). Variable protocols for monitoring incidence of pathogens among OECD countries remain. Nevertheless, there is evidence of sufficient standardization of monitoring protocols such as the European Surveillance System, which has contributed to reduce this gap.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".