Point-of-care tests for urinary tract infections: protocol for a systematic review and meta-analysis of diagnostic test accuracy
Bibliographic record
Abstract
INTRODUCTION: Urinary tract infections (UTIs) are the second most common type of infection worldwide, accounting for a large number of primary care consultations and antibiotic prescribing. Current diagnosis is based on an empirical approach, relying on symptoms and occasional use of urine dipsticks. The diagnostic reference standard is still urine culture, although it is not routinely recommended for uncomplicated UTIs in the community, due to time to diagnosis (48 hours). Faster point-of-care tests have been developed, but their diagnostic accuracy has not been compared. Our objective is to systematically review and meta-analyse the diagnostic accuracy of currently available point-of-care tests for UTIs. METHODS AND ANALYSIS: Studies evaluating the diagnostic accuracy of point-of-care tests for UTIs will be included. PubMed, Web of Science, Embase and Cochrane Database of Systematic Reviews were searched from inception to 1 June 2019. Data extraction and risk-of-bias assessment will be assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Meta-analysis will be performed depending on data availability and heterogeneity. ETHICS AND DISSEMINATION: This is a systematic review protocol and therefore formal ethical approval is not required, as no primary, identifiable, personal data will be collected. Patients or the public were not involved in the design of our research. However, the findings from this review will be shared with key stakeholders, including patient groups, clinicians and guideline developers, and will also be presented and national and international conferences. PROSPERO REGISTRATION NUMBER: CRD42018112019.
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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 | 0.074 | 0.121 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.023 | 0.034 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.055 | 0.006 |
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".