Quantitative human health risk assessments of antimicrobial use in animals and selection of resistance: a review of publicly available reports
Bibliographic record
Abstract
Quantitative risk assessments have been conducted to estimate the probability and magnitude of adverse human health effects from antimicrobial use in food animals through selection for antimicrobial resistance in bacteria. The majority focused on licensed antimicrobials under regulatory scrutiny, including growth promoters and agents of critical importance to human health. Most used models to attribute fractions of surveillance-derived estimates of antimicrobial-resistant infections in humans to antimicrobial use in animals. Risk estimates ranged from a few additional illnesses per million at risk, to many thousands. Although useful, published quantitative risk assessments have been unable to comprehensively address important aspects of antimicrobial resistance, including multiple exposure pathways, interrelationships among bacteria, co-selection, and cumulative effects of antimicrobial use in multiple species and countries. However, quantitative risk assessment shows promise for synthesis and analysis of scientific data. Work is required to develop methodology and train more risk analysts. An international forum is needed to pool expertise, review existing risk assessments and disseminate the results to risk managers throughout the world.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".