Quality indicators for appropriate antibiotic prescribing in urinary tract infections in children
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to define a set of urinary tract infections (UTIs)-specific quality indicators for appropriate prescribing in children and evaluate clinical practices in a district general hospital in Greece. METHODS: The UTIs-specific quality indicators were informed by a review of the existing literature. Quality indicators were selected to describe the overall antibiotics use, prescribing patterns and UTIs clinical management regarding treatment and prophylaxis in a cohort of children admitted with a UTI. Microbiological, clinical and prescribing data about dosing, duration and route of administration were collected from the patients' electronic health records. RESULTS: Twelve quality indicators were adapted or developed for prescribing in childhood UTIs. A broad variety of antibiotics were prescribed for UTIs, with a drug utilization (DU) 90% rate of 6 and 9 different antibiotics for febrile and afebrile UTIs, respectively. Despite the low incidence of multi-drug resistant UTIs in the study period (9/261, 3.4%), broad-spectrum antibiotics were prescribed in 33.5% (164/490) of prescriptions. A total of 62.8% (164/261) of patients were started on empiric combined therapies, while opportunities to de-escalate were missed in 37.8% (62/164) of them. One quarter (67/261, 25.7%) of patients did not fulfil the criteria for receiving treatment, while nearly half of those prescribed prophylaxis (82/175, 46.9%) could have avoided having a prophylaxis prescription. CONCLUSIONS: Our study identified substantial gaps for improvement in antimicrobial prescribing for UTIs in children. The application of the proposed quality indicators could help to limit unnecessary antibiotics use in children with UTI.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".