Epidemiology of Shiga Toxin-Producing Escherichia coli O157 in the Province of Alberta, Canada, 2009–2016
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Shiga toxin-producing Escherichia coli (STEC) infections are the product of the interaction between bacteria, phages, animals, humans, and the environment. In the late 1980s, Alberta had one of the highest incidences of STEC infections in North America. Herein, we revisit and contextualize the epidemiology of STEC O157 human infections in Alberta for the period 2009–2016. STEC O157 infections were concentrated in large urban centers, but also in rural areas with high cattle density. Hospitalization was often required when the Shiga toxin genotype stx2a stx2c was involved, however, only those aged 60 years or older and infection during spring months (April to June) independently predicted that need. Since the late 1980s, the rate of STEC O157-associated hemolytic uremic syndrome (HUS) in Alberta has remained unchanged at 5.1%, despite a marked drop in the overall incidence of the infection. While Shiga toxin genotypes stx1a stx2c and stx2a stx2c seemed associated with HUS, only those aged under 10 years and infection during spring months were independently predictive of that complication. The complexity of the current epidemiology of STEC O157 in Alberta highlights the need for a One Health approach for further progress to be made in mitigating STEC morbidity.
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.
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 | 0.000 | 0.000 |
| 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 it