Direct molecular detection of amoxicillin-susceptible <i>E. coli</i> in urine samples from children with suspected urinary tract infection: A potential tool to improve antibiotic stewardship and patient care
Bibliographic record
Abstract
Background: Rapid detection of amoxicillin-susceptible Escherichia coli (ASEC) urinary tract infections (UTIs) could have a significant impact on patient care and improve antibiotic stewardship. This is especially true for infants and children, for whom antibiotic choices are more limited than for adults. Methods: A real-time polymerase chain reaction (PCR) uniplex panel for detection of ASEC using PCR assays for E. coli and five resistance genes ( blaTEM, blaSHV, blaOXA, blaCTX-M, and blaCMY) and an internal control was designed. PCR was then performed directly on pediatric urine samples using an inhibitor-resistant DNA polymerase. The main outcome measure was the performance of the PCR panel (sensitivity, specificity, positive predictive value [PPV], negative predictive value [NPV], accuracy) for the detection of ASEC. ASEC samples were defined as those that were E. coli PCR positive and PCR negative for all five resistance genes. PCR results were compared with the reference standard for culture and susceptibility testing. Results: Two hundred and six urine samples with pyuria (>10 white blood cells/high power field) were tested with the PCR panel. Two samples showed PCR inhibition (1%). For ASEC detection, the PCR panel showed a sensitivity of 91.53% (95% CI 81.32% to 97.19%), specificity of 98.21% (95% CI 90.45% to 99.95%), PPV of 98.18% (95% CI 88.54% to 99.74%), NPV of 91.67% (95% CI 82.61% to 96.22%), and accuracy of 94.78% (95% CI 88.99% to 98.06%). Conclusions: This PCR method could potentially enable amoxicillin or ampicillin to be used in a greater proportion of children with E. coli UTIs, improving antibiotic stewardship.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".