Evaluation of Enzyme Immunoassays and Real-Time PCR for Detecting Shiga Toxin-Producing Escherichia coli in Southern Alberta,
Bibliographic record
Abstract
Two immunoassays (Shiga Toxin Chek and Shiga Toxin Quik Chek) and real-time PCR were used to detect Shiga toxin-produc-ing Escherichia coli. For enriched culture, the sensitivity and specificity of the three methods ranged from 80.0 % to 98.2 % and 98.0 % to 100.0%, respectively. STEC isolates were identified in 2.6 % of the 784 samples. Non-O157 Shiga toxin-producing Escherichia coli (STEC) is anemerging cause of enteric and systemic illness and account for 50 % of STEC infections (1, 2). Serotypes O104, O121, O26, O145 and O157 (1–6) have been linked to outbreaks. STEC-re-lated disease outcomes can result in hemolytic-uremic syndrome (HUS) (7–9) followed by other complications (10–13) affecting various organs (9, 12, 14–17). STEC can be transmitted via foods (3, 4, 18–20), water (21), animals (22–25), and from person to person (26–28). Ruminants are natural carriers of STEC and are considered the main reservoirs for these pathogens (29). Conventional culture methods focus mainly on the O157 se-rotype, and non-O157 STEC serotypes are underreported (30). The Centers for Disease Control and Prevention guidelines from October 2009 recommend simultaneous culture of stool samples and detection of Shiga toxins and/or their genes for all STEC isolates (31). Amplification and enzyme immunoassay (EIA) kits for STEC detection are commercially available (30,
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".