Emerging Public Health Challenges of Shiga Toxin–Producing<i>Escherichia coli</i>Related to Changes in the Pathogen, the Population, and the Environment
Bibliographic record
Abstract
Emerging public health challenges of Shiga toxin (stx)-producing Escherichia coli (STEC) include the occurrence of more frequent or severe disease and risk factors shifts associated with changes, often interconnected, in the pathogen, the population, and the environment. In 3 outbreaks with heightened severity attributed to enhanced pathogen virulence, including the acquisition of an stx2 phage in 1 outbreak, population and environmental factors likely contributed significantly to disease outcomes. Evolving population risk factors that are associated with more severe disease include consumption of fresh produce, contact with STEC-contaminated environments, demographics, socioeconomic status, and immunity. Risks of increasing STEC environmental pollution are related to continued intensification of agriculture and super-shedder cattle. Mitigation strategies include surveillance and research on emerging STEC, development of effective communications and public education strategies, and improved policies and interventions to mitigate risks, including those related to the contamination of produce and the environment, using a "One Health" approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".