Evaluation of a New Strategy for Clean-Catch Urine in Infants
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A new noninvasive bladder stimulation technique has been described to obtain clean-catch urine (CCU) in infants aged <30 days. Objectives were (1) to determine proportion and predictive factors for successful CCU collections using a stimulation maneuver technique among infants <6 months and (2) to determine the proportion of bacterial contamination with this method. METHODS: A prospective cohort study was conducted in a tertiary pediatric emergency department among infants <6 months needing a urine sample. CCU samples were collected using a standardized stimulation technique. Invasive technique was performed after CCU for three specific conditions. Primary outcomes were proportions of successful CCU specimens and bacterial contamination. We determined associations between successful urine samples and 4 predictive factors (age, sex, low oral intake, and recent voiding). RESULTS: A total of 126 infants were included (64 boys, median age: 55 days). The CCU procedure was effective in 62 infants (49%; median time: 45 seconds). Infants 0 to 29 days; 30 to 59 days, and 60 to 89 days had more successful procedures, compared with infants >89 days (odds ratios [95% confidence interval (CI)]: 4.3 [1.4 to 13.4]; 3.2 [1.2 to 8.4]; and 4.44 [1.5 to 13.3], respectively). The contamination proportion was 16% (95% CI: 8% to 27%) in the CCU group. This proportion was not statistically different compared with the invasive method group (6%, 95% CI: 3% to 15%). CONCLUSIONS: The CCU procedure is a quick and effective noninvasive method in children aged <90 days. Contamination proportions were similar to those reported in the literature for urethral catheterization. Circumstances for which the CCU procedure could be performed are proposed.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".