Factors Associated with Cross-Contamination of Hides of Scottish Cattle by <i>Escherichia coli</i> O157
Bibliographic record
Abstract
The putative source of hide contamination for 236 cattle in Scotland followed from the farm through to slaughter was determined using phage and verocytotoxin type data. The majority of cattle (84%) were found to have subtypes of Escherichia coli O157 on their hide that had not been found previously in any animal from the farm of origin, strongly suggesting that contamination occurred once animals had left the farm of origin. Using logistic regression analysis, several variables and factors were found to be strongly associated (P < 0.01) with cross-contamination of cattle hides at the univariate level; commercial transport to slaughter, transport with other animals, use of a crush, line automation, and increasing slaughterhouse throughput were all risk factors, while feeding hay in lairage, processing an animal earlier in a slaughter cohort, and cleaning the landing area poststunning were protective. In the multivariable model, with the slaughterhouse and the farm group included as random effects, factors associated with the cross-contamination of cattle hides were identified. Transport to the slaughterhouse by a commercial hauler had a borderline-significant association with increased odds of an animal having a cross-contaminated hide (odds ratio [OR] [95% confidence interval (CI)] = 5.7 [0.99, 33.0]; P = 0.05). At the slaughterhouse, providing hay to cattle waiting in lairage (OR [95% CI] = 0.04 [<0.01, 1.04]; P = 0.05) and cleaning the landing area (OR [95% CI] = 0.03 [<0.01, 1.15,]; P = 0.06) also had a borderline-significant association with decreased odds of an animal having a cross-contaminated hide. Although the prevalence of carcass contamination remains very low, targeted intervention at the preslaughter stage may have the potential to reduce further the risk to public health.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".