The Shiga toxin-producing Escherichia coli, their ruminant hosts, and potential on-farm interventions: a review
Bibliographic record
Abstract
The emergence of Shiga toxin-producing Escherichia coli serotype O157:H7 as a major human pathogen over the last 2 decades has focused attention on this organism’s ruminant hosts. Despite implementation of conventional control methods, people continue to become seriously ill from contaminated meat or other food products, manure-contaminated drinking and recreational water, and direct contact with ruminants. E. coli O157:H7 can cause life-threatening disease, and is a particular threat to children, through acute and chronic kidney damage. Compared with other food-borne bacteria, E. coli O157:H7 has a remarkably low infectious dose and is environmentally robust. Cattle are largely unaffected by this organism and have been identified as the major source of E. coli O157:H7 entering the human food chain. Other Shiga toxin-producing E. coli can be pathogenic to humans and there is increasing evidence that their significance has been underestimated. Governments around the world have acted to tighten food safety regulations, and to investigate animal sources and on-farm control of this and related organisms. Potential intervention strategies on-farm include: feed and water hygiene, altered feeding regimes, specific E. coli vaccines, antibacterials, antibiotics, probiotics, and biological agents or products such as bacteriophages, bacteriocins, or colicins.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".