CPHLN recommendations for the laboratory detection of Shiga toxin-producing Escherichia coli (O157 and non‑O157)
Bibliographic record
Abstract
O157:H7/NM (STEC O157) are the most commonly known STEC serotypes but it is now increasingly apparent that non-O157 STEC serotypes have been underreported in the past because they were not part of routine screening in many front-line laboratories. The Canadian Public Health Laboratory Network (CPHLN) has identified the need for improved detection and surveillance of non-O157 STEC and has developed the following recommendations to assist in the decision-making process for clinical and reference microbiology laboratories. These recommendations should be followed to the best of a laboratory's abilities based on the availability of technology and resources. The CPHLN recommends that when screening for the agents of bacterial gastroenteritis from a stool sample, front-line laboratories use either a chromogenic agar culture or a culture-independent diagnostic test (CIDT). CIDT options include nucleic acid amplification tests (NAATs) to detect Shiga toxin genes or enzyme immunoassays (EIAs) to detect Shiga toxins. If either CIDT method is positive for possible STEC, laboratories must have a mechanism to culture and isolate STEC in order to support both provincial and national surveillance as well as outbreak investigations and response. These CPHLN recommendations should result in improved detection of STEC in patients presenting with diarrhea, especially when due to the non-O157 serotypes. These measures should enhance the overall quality of healthcare and food safety, and provide better protection of the public via improved surveillance and outbreak detection and response.
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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 | 0.019 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".