Risk Factors for the Presence of High-Level Shedders of<i>Escherichia coli</i>O157 on Scottish Farms
Bibliographic record
Abstract
Escherichia coli O157 infections are the cause of sporadic or epidemic cases of often bloody diarrhea that can progress to hemolytic uremic syndrome (HUS), a systematic microvascular syndrome with predominantly renal and neurological complications. HUS is responsible for most deaths associated with E. coli O157 infection. From March 2002 to February 2004, approximately 13,000 fecal pat samples from 481 farms with finishing/store cattle throughout Scotland were examined for the presence of E. coli O157. A total of 441 fecal pats from 91 farms tested positive for E. coli O157. From the positive samples, a point estimate for high-level shedders was identified using mixture distribution analysis on counts of E. coli O157. Models were developed based on the confidence interval surrounding this point estimate (high-level shedder, greater than 10(3) or greater than 10(4) CFU g(-1) feces). The mean prevalence on high-level-shedding farms was higher than that on low-level-shedding farms. The presence of a high-level shedder on a farm was found to be associated with a high proportion of low-level shedding, consistent with the possibility of a higher level of transmission. Analysis of risk factors associated with the presence of a high-level shedder on a farm suggested the importance of the pathogen and individual host rather than the farm environment. The proportion of high-level shedders of phage 21/28 was higher than expected by chance. Management-related risk factors that were identified included the type of cattle (female breeding cattle) and cattle stress (movement and weaning), as opposed to environmental factors, such as water supply and feed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".