Clinical predictors of positive urine cultures in young children at risk for urinary tract infection
Bibliographic record
Abstract
BACKGROUND: Urinary tract infections (UTIs) are a common source of bacterial infection among young febrile children. The diagnosis of UTI is challenging because the clinical presentation is not specific. OBJECTIVE: To describe clinical predictors to identify young children needing urine culture for evaluation of UTI. METHODS: Retrospective cohort study of all children younger than two years of age (719 hospital visits for 545 patients) suspected of having a UTI during a 12-month period. The outcome was UTI, defined as a catheterized urine culture with pure growth of 10⁴ colonies/mL or greater, or suprapubic aspiration culture with 10³ colonies/mL or greater. Candidate predictors included demographic, historical and physical examination variables. RESULTS: The medical records of 545 children younger than two years of age were reviewed. Forty-six per cent were girls. Mean age was 9.1 months (SD 7 months). Four variables were found to predict UTI: absence of another source of fever on examination (odds ratio [OR]=41.6 [95% CI, 8.8 to 197.4]), foul smelling urine (OR=19.7 [95% CI, 5.7 to 68.2]), white blood cell count greater than 15,000/mm³ (OR=4.3 [95% CI, 2.0 to 9.3]), younger than six months old (OR=3.1 [95% CI, 1.3 to 7.1]). The sensitivity of an abnormal urine analysis was 0.77 (95% CI, 0.66 to 0.88) and the specificity was 0.31 (95% CI, 0.2 to 0.42). CONCLUSION: An incremental increase in risk for UTI is associated with younger age (younger than six months), having a white blood cell count higher than 15,000/mm³, parental report of malodorous or foul smelling urine and the absence of an alternative source of fever. In the present patient population, obtaining a urine culture from children with at least one of these clinical predictors would have resulted in missing one UTI (2%), and 111 negative cultures (20%) would have been avoided.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".