Do all Pediatric Urine Specimens Need to Go to the Laboratory?
Bibliographic record
Abstract
Objective: This study aimed to evaluate the accuracy of the urine dipstick in diagnosing UTIs in children at a tertiary care centre in Pakistan. Methods: 72 inpatients at the Aga Khan Hospital pediatric ward, getting laboratory urinalysis due to UTI suspicion, were included. Dipstick tests were done on the urine samples being sent to the lab for microscopy. The sensitivity, specificity and likelihood ratios (LRs) of dipstick LE, and pyuria and bacteriuria on microscopy were calculated and compared, using urine culture results as the gold standard for diagnosis. Results: The specificity of dipstick LE, pyuria and bacteriuria were 77%, 77% and 90% respectively, while the positive likelihood ratios were was 28%, 44% and 49% respectively. Urine cultures were done for 58 patients, with 5 positive cultures, so plausible estimates of sensitivity were not made. Conclusions: Urine microscopy is a more accurate screening test for ruling in UTIs than the dipstick. Keeping in mind its diagnostic limitations, the dipstick can be used to help rule in a UTI, although confirmation by cultures is recommended. Further studies are needed to validate these results in children and to evaluate the dipstick’s sensitivity for ruling out disease.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".