Evaluating iQ-CheckTM real-time PCR to detect <i>Salmonella</i> from poultry environmental samples in Fraser Valley, British Columbia, Canada
Bibliographic record
Abstract
Salmonella is a ubiquitous pathogen that accounts for foodborne and livestock illnesses worldwide. Robust surveillance programs must be implemented to maintain human and animal health and limit economic losses. The poultry industry in particular demands the implementation of rapid Salmonella detection methods that will facilitate the timely availability of results in a manner allowing actions to be taken for the associated poultry products. One such method, the iQ-CheckTM real-time PCR, has significantly reduced turnaround times compared to conventional culture methods. In this study, a total 733 poultry environmental samples was received from farms in the Fraser Valley of British Columbia, Canada and the real-time PCR method was assessed for its ability to detect Salmonella in comparison to the currently used culture protocol. The iQ-Check real-time PCR method was effective at accurately screening out the majority of negative samples, and demonstrated a very strong correlation with the culture method. This was especially true when selective enrichment was performed before PCR, with sensitivity, specificity, and accuracy values reaching 100.0%, 98.5%, and 98.9%, respectively. These results demonstrate that rapid detection methods could be effectively introduced into current Salmonella surveillance workflows dealing with environmental poultry samples to reduce turnaround times and minimize economic impacts on producers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".