Shiga toxin–producing Escherichia coli in British Columbia, 2011–2017: Analysis to inform exclusion guidelines
Bibliographic record
Abstract
BACKGROUND: O157:H7 was the most common serotype detected in the 1980s to 1990s, but improvements in laboratory methods have led to increased detection of non-O157 STEC. Non-O157 STEC producing only Stx1 tend to cause milder clinical illness. Exclusion guidelines restrict return to high-risk work or settings for STEC cases, but most do not differentiate between STEC serogroups and Stx type. OBJECTIVE: To analyze British Columbia (BC) laboratory and surveillance data to inform the BC STEC exclusion guideline. METHODS: For all STEC cases reported in BC in 2011-2017, laboratory and epidemiological data were obtained through provincial laboratory and reportable disease electronic systems, respectively. Incidence was measured for all STEC combined as well as by serogroup. Associations were measured between serogroups, Stx types and clinical outcomes. RESULTS: Over the seven year period, 984 cases of STEC were reported. A decrease in O157 incidence was observed, while non-O157 rates increased. The O157 serogroup was significantly associated with Stx2. Significant associations were observed between Stx2 and bloody diarrhea, hospitalization and HUS. CONCLUSION: The epidemiology of STEC has changed in BC as laboratories increasingly distinguish between O157 and non-O157 cases and identify Stx type. It appears that non-O157 cases with Stx1 are less severe than O157 cases with Stx2. The BC STEC exclusion guidelines were updated as a result of this analysis.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".