Diagnosis of urinary tract infections in the hospitalized older adult population in Alberta
Bibliographic record
Abstract
BACKGROUND: Urinary tract infections (UTIs) are one of the most common infections reported in older adults, across all settings. Although a diagnosis of a UTI requires specific clinical and microbiological criteria, many older adults are diagnosed with a UTI without meeting the diagnostic criteria, resulting in unnecessary antibiotic treatment and their potential side effects, and a failure to find the true cause of their presentation to hospital. OBJECTIVE: The aim of this study was to evaluate the accuracy of UTI diagnoses amongst hospitalized older adults based on clinical and microbiological findings, and their corresponding antibiotic treatment (including complications), in addition to identifying possible factors associated with a confirmed UTI diagnosis. METHODS: A single-center retrospective cross-sectional study of older adult patients (n = 238) hospitalized at the University of Alberta Hospital with an admission diagnosis of UTI over a one-year period was performed. RESULTS: 44.6% (n = 106) of patients had a diagnosis of UTI which was supported by documents clinical and microbiological findings while 43.3% (n = 103) of patients had bacteriuria without documented symptoms. 54.2% (n = 129) of all patients were treated with antibiotics, despite not having evidence to support a diagnosis of a UTI, with 15.9% (n = 37) of those patients experiencing complications including diarrhea, Clostridioides difficile infection, and thrush. History of major neurocognitive disorder was significantly associated with diagnosis of UTI (p = 0.003). CONCLUSION: UTIs are commonly misdiagnosed in hospitalized older adults by healthcare providers, resulting in the majority of such patients receiving unnecessary antibiotics, increasing the risk of complications. These findings will allow for initiatives to educate clinicians on the importance of UTI diagnosis in an older adult population and appropriately prescribing antibiotics to prevent unwanted complications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".