Performance of commercial tests for molecular detection of Shiga toxin-producing <i>Escherichia coli</i> (STEC): a systematic review and meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: (STEC) enables appropriate treatment. Numerous commercially available molecular tests exist, but they vary in clinical performance. This systematic review aims to synthesise available evidence to compare the clinical performance of enzyme immunoassay (EIA) and nucleic acid amplification tests (NAATs) for the detection of STEC. METHODS AND ANALYSIS: The following databases will be searched employing a standardised search strategy: Medline, Embase, Cochrane CENTRAL Register of Controlled Trials, Cochrane Database of Systematic Reviews, PubMed, Scopus and Web of Science. Grey literature will be searched under advice from a medical librarian. Independent reviewers will screen titles, abstracts and full texts of retrieved studies for relevant studies. Data will be extracted independently by two reviewers, using a piloted template. Quality Assessment of Diagnostic Accuracy Studies-2 will be employed to assess the risk of bias of individual studies, and the quality of evidence will be assessed with the Grading of Recommendations Assessment, Development and Evaluation approach. A bivariate random-effects model will be used to meta-analyse the sensitivity and specificity of commercial STEC diagnostic tests, and a hierarchical summary receiver operator characteristic curve will be constructed. Studies of single test accuracy of EIA and NAATs and studies of comparative accuracy will be analysed separately. ETHICS AND DISSEMINATION: Ethics approval was not required for this systematic review and meta-analysis. Findings will be disseminated in conferences, through a peer-reviewed journal and via personal interactions with relevant stakeholders. PROSPERO REGISTRATION NUMBER: CRD42018099119.
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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 | 0.078 | 0.080 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.022 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.005 |
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".