Detection and Prevalence of Verotoxin-Producing Escherichia coli O157 and Non-O157 Serotypes in a Canadian Watershed
Bibliographic record
Abstract
Verotoxin-producing Escherichia coli (VTEC) strains are the cause of food-borne and waterborne illnesses around the world. Traditionally, surveillance of the human population as well as the environment has focused on the detection of E. coli O157:H7. Recently, increasing recognition of non-O157 VTEC strains as human pathogens and the German O104:H4 food-borne outbreak have illustrated the importance of considering the broader group of VTEC organisms from a public health perspective. This study presents the results of a comparison of three methods for the detection of VTEC in surface water, highlighting the efficacy of a direct VT immunoblotting method without broth enrichment for detection and isolation of O157 and non-O157 VTEC strains. The direct immunoblot method eliminates the need for an enrichment step or the use of immunomagnetic separation. This method was developed after 4 years of detecting low frequencies (1%) of E. coli O157:H7 in surface water in a Canadian watershed, situated within one of the FoodNet Canada integrated surveillance sites. By the direct immunoblot method, VTEC prevalence estimates ranged from 11 to 35% for this watershed, and E. coli O157:H7 prevalence increased to 4% (due to improved method sensitivity). This direct testing method provides an efficient means to enhance our understanding of the prevalence and types of VTEC in the environment. This study employed a rapid evidence assessment (REA) approach to frame the watershed findings with watershed E. coli O157:H7 prevalences reported in the literature since 1990 and the knowledge gap with respect to VTEC detection in surface waters.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".