Shiga-Toxigenic Escherichia coli Detection in Stool Samples Screened for Viral Gastroenteritis in Alberta, Canada
Bibliographic record
Abstract
Shiga-toxigenic Escherichia coli (STEC) is an important cause of diarrheal disease. The most notorious STEC serotype is O157:H7, which is associated with hemorrhagic colitis and hemolytic-uremic syndrome (HUS). As a result, this serotype is routinely screened for in clinical microbiology laboratories. With the bias toward the identification of the O157 serogroup in routine diagnostic processes, non-O157 STEC has been largely underrepresented in the epidemiology of STEC infections. This diagnostic bias is further complicated by the fact that many non-O157 STEC infections cause nonspecific gastroenteritis symptoms reminiscent of enteric viral infections. In this study, real-time PCR was used to amplify Shiga toxin genetic determinants (stx(1) and stx(2)) from enriched stool samples that were initially submitted for the testing of enteric viruses in patients with suspected viral gastroenteritis between May and September of 2006, 2007, and 2008 (n = 2,702). Samples were submitted from the province of Alberta, Yukon, the Northwest Territories, and Nunavut, Canada. A total of 38 samples (1.4%) tested positive for Shiga toxin genes, and 15 isolates were cultured for further characterization. Several of the serotypes identified (O157:H7, O26:HNM, O26:H11, O103:H25, O121:H19, and O145:HNM) have been previously associated with outbreaks and HUS. This study outlines the importance of combining molecular methods with classical culture techniques to enhance the detection of emerging non-O157 as well as O157 serotypes in diarrheal stool samples. Furthermore, atypical diarrhea disease caused by non-O157 STEC can be routinely missed due to screening only for viral agents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".