Urinary tract infection in elderly trauma patients
Bibliographic record
Abstract
BACKGROUND: Elderly trauma patients are at high risk for urinary tract infection (UTI). Despite this, UTI has been deemed a potentially preventable problem and therefore not reimbursable by the Centers for Medicare and Medicaid Services. Early identification of UTI in these patients should lead to prompt treatment, improved outcomes, and cost savings. Risk factors for UTI development in this population must be elucidated to realize these goals. METHODS: The Trauma Quality Improvement Program (TQIP) database was used to analyze elderly patients (≥65 years) admitted as a result of injury during 2011. Patients with genitourinary injuries or undergoing dialysis before admission were excluded. Multivariable logistic regression analysis was conducted to identify UTI risk factors. Mean cost of UTI was calculated based on the assumption of $862 to $1,007 per UTI. RESULTS: In total, 33,257 patients were identified; 1,492 developed UTI (4.5%). Multiple significant risk factors were identified, including age greater than 75 years, female sex, ascites, moderate head injury, impaired sensorium, congestive heart failure, and duration of hospital stay (all p < 0.05). Assuming that UTIs diagnosed on hospital Day 1 were preexisting, the cost of UTI to TQIP hospitals ranged from $1,280,959 to $1,496,434 per year. CONCLUSION: Duration of stay has a profound impact on the development of UTIs in elderly trauma patients, but overall severity of injury does not. In addition, multiple nonmodifiable risk factors were identified, prompting the possibility for increased screening of occult UTIs. Reimbursement for care of UTI in this complicated patient population should be revisited. The TQIP database must improve urinary catheter data. LEVEL OF EVIDENCE: Epidemiologic study, level III.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".