Transmission and control of <i>Escherichia coli</i> O157:H7 — A review
Bibliographic record
Abstract
Escherichia coli 157:H7 has evolved as an important foodborne pathogen since its initial description in 1982. Outbreaks of illness associated with E. coli O157:H7 have been reported throughout the northern hemisphere, most frequently in Canada, the United States, Japan, and the United Kingdom. In Canada, infections due to E. coli O157: H7 appear to be more common in the western provinces than in the east, in rural vs. urban environments, and during summer as opposed to winter months. Undercooked ground beef has been implicated as the primary vehicle in E. coli O157:H7 infection, but contaminated fruits, vegetables and water have also been linked to E. coli O157:H7 outbreaks. Epidemiological investigations demonstrate that dairy and beef cattle are primary reservoirs of this organism, carrying it asymptomatically and shedding it intermittently and seasonally in their feces. Surveys in Canada and the United States indicate widespread distribution of E. coli O157:H7 in cattle operations. The prevalence of E. coli O157:H7 in cattle has been increasing in recent reports, likely due to the development of more sensitive methods for the detection of the organism. Escherichia coli O157:H7 has been isolated from feed, water for livestock, manure, soil and flies, all of which represent potential sources of contamination for cattle and their environment. To date, effective methods for controlling E. coli O157:H7 in cattle have not been identified, although dietary manipulation, vaccination and bacteriophage therapy have been reported to have potential as intervention strategies. Effective control of E. coli O157:H7 requires reducing the frequency and intensity of fecal shedding of this pathogen by cattle, in addition to targeting environmental sources of the organism. Key words: Escherichia coli O157:H7, cattle, sources, diet, transmission
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".