Urinary Tract Infection Investigation and Treatment in Older Adults Presenting to the Emergency Department with Confusion: a Health Record Review of Local Practice Patterns
Bibliographic record
Abstract
Background The rate of urinary tract infection (UTI) investigation and treatment in confused older emergency department (ED) patients has not been described in the literature. We aim to describe the pattern of practice in an academic tertiary care ED for this common presentation. Methods A health record review was conducted on 499 adults aged ≥65 presenting to academic EDs with confusion. Exclusion criteria: Glasgow Coma Scale < 13, current treatment for UTI, indwelling catheters, nephrostomy tubes, transfer from another hospital. Outcomes were the prevalence of UTI investigation, diagnosis and antibiotic treatment. Results 64.9% received urine tests, 11.4% were diagnosed with UTI, and 35.2% were prescribed antibiotics. In the subgroup with no urinary symptoms, fever, or other obvious indication for antibiotics, these numbers were 58.2%, 7.6%, and 18.1%, respectively. Patients who had urine tests or received antibiotics were older than those who did not (p values < .01). Patients receiving antibiotics had higher admission rates and 30-day and six-month mortality (OR of 2.9 [2.0–4.3], 4.0 [1.6–11], and 2.8 [1.4–5.8], respectively). Conclusion Older patients presenting to ED with confusion were frequently investigated and treated for UTI, even in the absence of urinary symptoms. Antibiotic treatment was associated with higher hospitalization and mortality.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".