Accuracy of molecular diagnostic techniques in patients with a confirmed urine culture: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: We aimed to identify the molecular diagnostic techniques available for urinary tract infection (UTI) diagnosis and their accuracy compared to traditional urinary culture. METHODS: A systematic search was performed in MEDLINE (OVID), EMBASE, LILACS, and the Cochrane Central Register of Controlled Trials (CENTRAL). The populations were adult and pediatric patients with confirmed UTI by reference standard urine culture. The index test for the diagnosis of UTI was any molecular diagnostic technique. The primary outcome was the diagnosis of UTI with measures of sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively), positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR), and area under the curve (AUC). The operative characteristics were determined, and a meta-analysis was performed. The evaluation of each included study was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. RESULTS: We identified 1230 studies with the search strategies. Ultimately, 13 studies met the inclusion criteria for qualitative analysis, and seven were included for the meta-analysis. Four molecular techniques were identified; however, it was only possible to synthesize the information from two of them. In multiplex polymerase chain reaction (PCR) meta-analysis, overall sensitivity was 0.80 (95% confidence interval [CI] 0.73-0.86) and specificity was 0.83 (95% CI 0.52-0.95). For the DOR, the overall result was 21 (95% CI 4.8-95). For reverse transcription (RT)-PCR, sensitivity was 0.94 (95% CI 0.73-0.99) and specificity was 0.59 (95% CI 0.063-0.96). For the DOR, the overall result was 23 (95% CI 1.1-467). CONCLUSIONS: Multiplex PCR and RT-PCR are molecular techniques that might be comparable to standard urine culture for UTI diagnosis. Refinement of these new diagnostic tools will avoid unnecessary antimicrobial therapy and the consequent development of drug-resistant resistant pathogens, as well as improve the ability to identify patients at risk and prevent or minimize sequelae derived from the infection.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".