Developing a Tool for Prospective Assessment of Treatment Appropriateness in Urinary Tract Infections
Bibliographic record
Abstract
Background: Antimicrobial resistance is an increasingly serious threat to global public health. Antimicrobial stewardship programs need to identify inappropriate antibiotic use patterns and offer practical recommendations to prescribers and institutions. Urinary tract infection (UTI) is a common syndrome for which a standardized tool would be useful when treatment appropriateness is assessed. To date, few UTI treatment assessment tools have been published, and the available tools do not support appropriateness assessment against published guidelines, or consistent adjudication from one auditor to another. Objective: To develop a tool for auditing UTI antibiotic therapy that assesses treatment appropriateness based on guideline concordance, and with high inter-rater reliability. Methods: An audit tool was developed iteratively by the local antimicrobial stewardship team. Two auditors used the tool to adjudicate treatment appropriateness in a sample of UTI cases against local treatment guidelines. Inter-rater agreement was estimated with Cohen’s kappa statistic. Results: The final design of the tool had individual sections for evaluating five aspects of treatment appropriateness, depending on the stage at which a patient was in his or her course of antibiotic therapy: diagnosis, empiric therapy, culture-directed therapy, route of antimicrobial administration, and duration of therapy. A total of 50 cases were assessed; among these, the two auditors agreed on 45 cases (90% agreement). The estimated kappa was 0.8. Conclusion: A unique tool with substantial inter-rater agreement was developed for assessing appropriateness of antimicrobial therapy in UTI. The process and design features that were outlined can be adapted by other antimicrobial stewardship programs to monitor antimicrobial use and improve quality of 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 | 0.155 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".