Factors Affecting Detection of Hepatitis E Virus on Canadian Retail Pork Chops and Pork Livers Assayed Using Real‐Time <scp>RT</scp>‐<scp>PCR</scp>
Bibliographic record
Abstract
We collected 599 Canadian retail pork chops and 283 pork livers routinely (usually weekly) from April 2011 to March 2012 using the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) retail sampling platform. Samples were assayed using validated real-time (q) reverse transcriptase polymerase chain reaction (RT-PCR) and nested classical RT-PCR for the detection of hepatitis E virus (HEV), porcine enteric calicivirus (PEC) and rotavirus (RV). The presence of Escherichia coli, Salmonella spp. and Campylobacter spp. was measured on a subset of our samples. Exact logistic regression models were fitted for predictors for HEV detection, for each assay. For both assays, sample type (pork chop versus liver) was a significant predictor for HEV RNA detection. For nested classical RT-PCR but not qRT-PCR, region of sample collection was a significant predictor (P = 0.008) of HEV detection. Odds of HEV detection were greatest in spring relative to other seasons. E. coli was a significant predictor for HEV RNA detection using the qRT-PCR (P = 0.03). Overall, the prevalence of E. coli, Salmonella spp. and Campylobacter spp. was significantly greater than HEV, PEC or RV on our retail pork samples. Our sparse data set for the detection of PEC and RV precluded modelling of risk factors for the detection of these viruses.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".