Assessment of Diagnostic Tools for Identifying Cattle Shedding and Super-Shedding <i>Escherichia coli</i> O157:H7 in a Longitudinal Study of Naturally Infected Feedlot Steers in Ohio
Bibliographic record
Abstract
The objectives of this study were to compare the performance of different diagnostic protocols (rectoanal mucosal swabs and immunomagnetic separation [RAMS-IMS], fecal samples and IMS [fecal-IMS], and direct plating) to determine the prevalence of Escherichia coli O157:H7 and to evaluate the pattern of E. coli O157:H7 shedding and super-shedding (defined as having a direct plating count equal to or >10(4) colony forming units of E. coli O157:H7 per gram of feces) in a longitudinal study of naturally infected feedlot steers. RAMS and fecal grab samples were obtained at 14-day intervals from 168 Angus-cross beef steers over a period of 22 weeks. Fecal samples were assessed by direct plating and IMS, whereas RAMS were tested only by enrichment followed by IMS to recover E. coli O157:H7. The period prevalence for shedding was high (62%) among feedlot steers and super-shedding was higher (23%) than anticipated. Although direct plating was the least sensitive method to detect E. coli O157:H7-positive samples, over 20% of high bacterial load samples were not detected by RAMS-IMS and/or fecal-IMS. The sensitivity of RAMS-IMS, fecal-IMS, and direct plating protocols was estimated using simple and multilevel mixed-effects logistic regression models, in which the dependent variable was the dichotomous results of each test and gold standard (i.e., parallel interpretation of the three protocols)-positive individuals were included as an independent variable along with other factors such as dietary supplements, time of sampling, and being exposed to a super-shedding pen-mate. The associations between these factors and the sensitivity of the diagnostic protocols were not statistically significant. In conclusion, differences in the reported impact of diet and probiotics on the shedding of E. coli O157:H7 in previous studies using RAMS-IMS or fecal-IMS were unlikely due to their impact on test performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".