Comparison of Clinical and Epidemiological Features of Shiga Toxin‐Producing <i>Escherichia coli</i> O157 and Non‐O157 Infections in British Columbia, 2009 to 2011
Bibliographic record
Abstract
INTRODUCTION: Shiga toxin-producing Escherichia coli (STEC) are major foodborne agents that have the potential to cause severe enteric illnesses and large outbreaks worldwide. Several studies found non-O157 infections to be clinically milder than O157 STEC infections. OBJECTIVE: To compare the clinical and epidemiological profiles of O157 and non-O157 STEC human infections in British Columbia (BC). METHODS: All STEC cases reported in BC from 2009 to 2011 by four local health authorities were included in the study. Cases were classified according to STEC serotype based on laboratory information. Information was gathered via case interview forms. Data analysis included the χ(2) test and Mann-Whitney test; P<0.05 was considered to be statistically significant. RESULTS: A total of 260 STEC cases were reported, including 154 (59.2%) O157 cases, 63 (24.2%) non-O157 cases and 43 (16.5%) STEC cases with no serotype identified. Hospitalization rate was higher and duration of hospitalization was significantly longer for O157 cases compared with non-O157 cases, but other clinical features were not significantly different. Patients with non-O157 infections were significantly more likely to have travelled outside Canada, less likely to report food exposure at social gatherings and more likely to consume bagged greens and cheese. DISCUSSION: O157 is the predominant O serotype in BC and appeared to be more clinically severe than non-O157 STEC infections. However, the true incidence and severity of non-O157 remain unknown due to our current inability to detect all non-O157 cases. The present study and the literature suggest the need to identify more predictive virulence factors because serotype does not consistently predict disease severity.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".