Intraurethral Lidocaine for Urethral Catheterization in Children: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVES: To determine whether lidocaine is superior to nonanesthetic lubricant (NAL) for relieving pain in children undergoing urethral catheterization (UC). METHODS: Children 0 to 24 months requiring UC were randomized to NAL or topical and intraurethral 2% lidocaine gel. Primary outcome was facial grimacing in the pre to during drug administration and catheterization phases. Secondary outcome was caregiver satisfaction by using a Visual Analog Scale. RESULTS: There were 133 participants (n = 68 lidocaine, n = 65 NAL). There were no significant differences in mean (SD) scores during UC between lidocaine and NAL (86.4% [121.5%] vs 85.2% [126.6%]), respectively (Δ [confidence interval (CI)] = -1.2 [-21.0 to 49.0], P = .4). There was a significantly greater difference in mean (SD) scores during instillation of lidocaine versus NAL (61.8% [105.6%] vs 3.2% [84.9%]), respectively (Δ [CI] -58.6 [-95.0 to -32.0], P < .001). There were no significant differences in mean (SD) parental satisfaction scores between lidocaine and NAL (4.8 [3.2] vs 5.9 [2.9]), respectively (CI-0.1 to 2.2; P = .06). In the subgroup analysis, age, gender, and positive urine culture did not significantly influence between-group differences in facial grimacing. CONCLUSIONS: Compared with NAL, topical and intraurethral lidocaine is not associated with significant pain reduction during UC, but significantly greater pain during instillation. Therefore, clinicians may consider using noninvasive pain-reducing strategies for young children who require UC.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".