An assessment of the human health impact of seven leading foodborne pathogens in the United States using disability adjusted life years
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We explored the overall impact of foodborne disease caused by seven leading foodborne pathogens in the United States using the disability adjusted life year (DALY). We defined health states for each pathogen (acute illness and sequelae) and estimated the average annual incidence of each health state using data from public health surveillance and previously published estimates from studies in the United States, Canada and Europe. These pathogens caused about 112 000 DALYs annually due to foodborne illnesses acquired in the United States. Non-typhoidal Salmonella (32 900) and Toxoplasma (32 700) caused the most DALYs, followed by Campylobacter (22 500), norovirus (9900), Listeria monocytogenes (8800), Clostridium perfringens (4000), and Escherichia coli O157 (1200). These estimates can be used to prioritize food safety interventions. Future estimates of the burden of foodborne disease in DALYs would be improved by addressing important data gaps and by the development and validation of US-specific disability weights for foodborne diseases.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it