Collecting the golden water: Quality assessment on approach of diagnosing urinary tract infections in 0 to 36 months old children
Bibliographic record
Abstract
OBJECTIVE: The study aimed to assess current practices of a community hospital for collection of urine sample when diagnosis of urinary tract infection (UTI) is suspected in children aged 0 to 36 months old. METHODS: An analysis of paediatric patients aged 0 to 36 months old was performed in two separate audits to assess the quality of urine sampling. The first, retrospective analysis comprised of urine collections techniques in a community hospital for diagnosis of UTI followed by an education intervention in which the hospital staff was briefed regarding the Canadian Paediatric Society (CPS) position statement for diagnosis and management of UTI. CPS recommendations were transposed using PowerPoint presentations, reminders at unit huddles, and other educational forums. Second audit was a prospective analysis which was conducted 6 months after the educations intervention. RESULTS: Bagged sampling had higher sensitivity and lower specificity due to sample contamination, versus transurethral bladder catheterization and suprapubic aspiration. The first audit showed that while 66% of culture-positive urine sampling was performed via the bagging, only 26% those positive cultures were repeated before treatment. In the second audit, after educational intervention, 33% of culture-positive urine collection was done via the bagging method and repeat testing was done in 83% of positive results on a bagged sample before initiating treatment. The false-positive rate for the diagnosis of UTIs in the first and second audit was 65.7 and 60%, respectively. CONCLUSION: Our study recognizes the flaws in community hospital practices in the diagnosis of UTI in children and validates the significance of educational intervention in improving health care.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".