Evaluation of a New Chromogenic Agar Medium for Detection of Shiga Toxin-Producing Escherichia coli (STEC) and Relative
Bibliographic record
Abstract
This study assesses the detection performance of CHROMagar STECmedium relative to a reference cytotoxin assay and de-scribes the current relative prevalence of O157 and non-O157 Shiga toxin-producing Escherichia coli (STEC) serotypes within the province of Manitoba, Canada. Over a 10-month period, 205 nonfrozen routine stool submissions to Cadham Provincial Laboratory (CPL) were used to assess the performance of CHROMagar STEC. Of the 205 stools, 14 were identified as true posi-tives by a cytotoxin assay, with resultant CHROMagar STEC sensitivity, specificity, and positive predictive and negative predic-tive values of 85.7%, 95.8%, 60.0%, and 98.9%, respectively. Using a separate panel of 111 STEC strains, CHROMagar STEC was shown to support the growth of 96 (86.5%) isolates. To assess relative prevalence, attempts were made to isolate by any means all STEC strains identified at CPL over a 17-month period. Of 49 isolates (representing 86.0 % of all STEC infections detected), only 28.6%were O157 STEC strains. Of the 35 non-O157 STEC strains, 29 were subjected to further molecular analysis. In contrast to earlier results from our area, carriage of stx2 appears to have increased. Overall, although CHROMagar STEC is not recom-mended as a primary screen, our results indicate that it is an effective supplemental medium for the isolation of probable STEC strains. Increased isolation of these serotypes is warranted to better understand their prevalence, clinical characteristics, and epidemiology and aid in the development or enhancement of food safety control programs targeting all STEC serotypes. Shiga toxin-producing Escherichia coli (STEC) emerged inNorth America in 1983 as a ground beef-associated outbreak of serotype O157:H7. The clear link between this serotype and
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".