A Novel Definition of Urinary Tract Infection across a National Primary Care Network
Bibliographic record
Abstract
Context Misdiagnosis and over-diagnosis of urinary tract infection (UTI) are a leading cause of inappropriate antimicrobial use. It is challenging to study UTI in the outpatient setting, as no case definition exists. Objective To develop and validate a novel electronic medical records (EMR)-based UTI case definition that can be applied to elucidate and understand UTI treatment and practice patterns in primary care. Study Design and Analysis Retrospective cross-sectional study. Setting or Dataset De-identified data from 268 primary care providers participating in the Manitoba Primary Care Research Network (MaPCReN), the Manitoba network within CPCSSN. Population Studied Providers participating in MaPCReN represented 306,394 patients with ≥1 visit between January 1, 2016 and December 31, 2021. A reference set included n=854 randomly selected adults aged ≥60 years, including 148 (17.3%) patients with ≥1 UTI encounter and 703 (82.6%) patients with no UTI encounters. In total there were 266 encounters for a UTI categorized as Criteria A (urinary symptoms documented), Criteria B (non-specific urinary symptoms) or Criteria C (unclear or no symptoms documented). Intervention/Instrument We assessed agreement of episode-specific case definitions compared to the reference set using sensitivity (Sen), specificity (Spec), positive predictive value (PPV), negative predictive value (NPV). We applied the validated case definition to the CPCSSN dataset to estimate prevalence and 95% confidence intervals using exact binomial test. Results The validation sample (n=854) consisted of 56.7% females and a mean age 76 years. The sen, spec, PPV, and NPV were: 88.4% (83.9-91.9), 89.2% (86.8-91.4), 74.4% (69.2-79.1), and 95.6 (93.8-97.0) respectively. Criteria A episodes had stronger agreement; 88.89% (83.66-92.90), 82.93% (80.17-85.44), 55.70% (50.03-61.26), and 96.87% (95.29-98.03) when compared to criteria A and B; 88.78% (83.64-92.75), 57.19% (51.50-62.74), 57.59% (51.94-63.11), and 88.61% (83.41-92.64), or criteria C; 82.98% (69.19-92.35), 71.47% (68.52-74.30), 12.34% (8.93-16.48), and 98.86% (97.77-99.51). Conclusions We developed and validated a novel EMR-based UTI case definition using a pan-Canadian primary care dataset. This case definition will facilitate research on UTI treatment in community-dwelling older adults and inform Quality Improvement initiatives that align clinical practice with evidence based guidelines.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".