Comparison of Common Enrichment Broths Used in Diagnostic Laboratories for Shiga Toxin—Producing Escherichia coli
Bibliographic record
Abstract
Acute gastroenteritis caused by Shiga toxin-producing Escherichia coli (STEC) affects more than 4 million individuals in Canada. Diagnostic laboratories are shifting towards culture-independent diagnostic testing; however, recovery of STEC remains an important aspect of surveillance programs. The objective of this study was to compare common broth media used for the enrichment of STEC. Clinical isolates including O157:H7 as well as non-O157 serotypes were cultured in tryptic soy (TSB), MacConkey (Mac), and Gram-negative (GN) broths and growth was compared using culture on sheep’s blood agar and real-time PCR (qPCR). In addition, a selection of the same isolates was spiked into negative stool and enriched in the same three broths, which were then evaluated using culture on CHROMagarTM STEC agar and qPCR. TSB was found to provide the optimal enrichment for growth of isolates with and without stool. The results from this study suggest that diagnostic laboratories may benefit from enriching STEC samples in TSB as a first line enrichment instead of GN or Mac.
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How this classification was reachedexpand
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".