Diagnostic Test Accuracy of Commercial Tests for Detection of Shiga Toxin–Producing Escherichia coli: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Rapid detection of Shiga toxin-producing Escherichia coli (STEC) enables appropriate monitoring and treatment. We synthesized available evidence to compare the performance of enzyme immunoassay (EIA) and PCR tests for the detection of STEC. METHODS: We searched published and gray literature for studies of STEC EIA and/or PCR diagnostic test accuracy relative to reference standards including at least one nucleic acid amplification test. Two reviewers independently screened studies, extracted data, and assessed quality with the second version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Bivariate random effects models were used to meta-analyze the clinical sensitivity and specificity of commercial EIA and PCR STEC diagnostic tests, and summary receiver operator characteristic curves were constructed. We evaluated the certainty of evidence with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. RESULTS: We identified 43 articles reflecting 25 260 specimens. Meta-analysis of EIA and PCR accuracy included 25 and 22 articles, respectively. STEC EIA pooled sensitivity and specificity were 0.681 (95% CI, 0.571-0.773; very low certainty of evidence) and 1.00 (95% CI, 0.998-1.00; moderate certainty of evidence), respectively. STEC PCR pooled sensitivity and specificity were 1.00 (95% CI, 0.904-1.00; low certainty of evidence) and 0.999 (95% CI, 0.997-0.999; low certainty of evidence), respectively. Certainty of evidence was downgraded because of high risk of bias. CONCLUSIONS: PCR tests to identify the presence of STEC are more sensitive than EIA tests, with no meaningful loss of specificity. However, given the low certainty of evidence, our results may overestimate the difference in performance.
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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 | 0.002 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".