Clinical Outcomes and Virulence Factors of Shiga Toxin-Producing Escherichia coli (STEC) from Southern Alberta, Canada, from 2020 to 2022
Bibliographic record
Abstract
Shiga toxin-producing Escherichia coli (STEC) can cause severe clinical disease in humans, particularly in young children. Recent advances have led to greater availability of sequencing technologies. We sought to use whole genome sequencing data to identify the presence or absence of known virulence factors in all clinical isolates submitted to our laboratory from Southern Alberta dated 2020–2022 and correlate these virulence factors with clinical outcomes obtained through chart review. Overall, the majority of HUS and hospitalizations were seen in patients with O157:H7 serotypes, and HUS cases were primarily in young children. The frequency of virulence factors differed between O157:H7 and non-O157 serotypes. Within the O157:H7 cases, certain virulence factors, including espP, espX1, and katP, were more frequent in HUS cases. The number of samples was too low to determine statistical significance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".