Phage-Based Typing Scheme for <i>Salmonella enterica</i> Serovar Heidelberg, a Causative Agent of Food Poisonings in Canada
Bibliographic record
Abstract
Salmonella enterica serovar Heidelberg is perhaps the second most frequent Salmonella serovar isolated from humans and the most common isolated from animals in Canada. This pathogen has shown increasing resistance to antimicrobial agents and mimics the multidrug resistance observed in S. enterica serovar Typhimurium strain DT 104. However, unlike for serovar Typhimurium, a rapid and inexpensive subtyping method has not been available for large-scale surveillance efforts. We developed a phage typing scheme and subtyped 2,523 strains of serovar Heidelberg from outbreaks, sporadic infections, and environmental sources in Canada between January 1991 and December 2000. All strains were sensitive to one or more phages and could be subdivided into 49 phage types. A total of 196 isolates from 13 major outbreaks could be subtyped into six phage types, while 86 strains from family outbreaks were assigned to seven phage types. All strains were typeable, and epidemiologically related strains isolated from patients and implicated foods had identical phage types, antibiograms, and pulsed-field gel electrophoresis (PFGE) patterns. Combining PFGE with phage typing increased the discriminatory power of the analysis beyond that of either method alone. We concluded that this phage typing scheme, in conjunction with PFGE, enhances subtyping of serovar Heidelberg strains. Furthermore, this phage typing scheme is a rapid, economical, stable, and reliable epidemiologic tool for tracing the origin of food-borne disease and for the surveillance of sporadic infections.
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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 | 0.000 |
| 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".