Epidemiology of Non-O157 Shiga Toxin-Producing Escherichia coli in the Province of Alberta, Canada, from 2018 to 2021
Bibliographic record
Abstract
Non-O157 serogroups contribute significantly to the burden of disease caused by Shiga toxin-producing Escherichia coli (STEC) and have been underrecognized by traditional detection algorithms. We described the epidemiology of non-O157 STEC in Alberta, Canada for the period of 2018 to 2021. All non-O157 STEC isolated from clinical samples were submitted for serotyping and qPCR targeting the stx1 and stx2 genes. A total of 729 isolates were identified. Increased detection occurred over the summer months, peaking in July. Patients 18 years and younger made up 42.4% of cases, with 31.1% in those 0–9 years of age. There was a slight female predominance (399/729, 54.7%) A total of 50 different serogroups were detected; the most common were O26 (30.3%), O103 (15.9%), O111 (12.8%), O121 (11.0%), O118 (3.3%) and O71 (2.9%). These six serogroups made up 76.2% of all isolates. In total, 567 (77.8%) were positive for stx1, 114 (15.6%) were positive for stx2 and 48 (6.6%) were positive for both stx1 and stx2. A wide variety of non-O157 serogroups have been detected in Alberta, with the most frequent serogroups differing from other locations. These results highlight the need for further characterization of their virulence factors and clinical impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".