Predictors of Long Length of Stay in Infants Hospitalized With Urinary Tract Infection
Bibliographic record
Abstract
OBJECTIVE: Urinary tract infection (UTI) is the most common serious bacterial infection in infants. To use resources optimally, factors contributing to costs through length of stay (LOS) must be identified. This study sought to identify clinical and health system factors associated with long LOS in infants with UTI. METHODS: Using a case-control design, we included infants <6 months old hospitalized with UTI. Cases had LOS ≥96 hours; controls had LOS <96 hours. Clinical and health system variables were extracted from medical records. Cases and controls were compared by using comparative statistics and multiple logistic regression analysis. RESULTS: Cases (n = 71) and controls (n = 71) did not differ by gender; cases were more likely to be younger (4.2 vs 7.1 weeks, P = .04), born preterm (13% vs 3%, P = .03), have known genitourinary disease (17% vs 4%, P = .01), an ultrasound (85% vs 68%, P = .02) or voiding cystourethrogram (VCUG) (61% vs 34%, P = .001) ordered, longer wait for VCUG (53 vs 27 hours, P = .002), consult requested (54% vs 10%, P < .001), and longer duration of intravenous (IV) antibiotics (125 vs 62 hours, P < .001). Multiple logistic regression demonstrated that cases were more likely to be premature (odds ratio [OR] 7.6), have known genitourinary disease (OR 7.3), and have VCUG ordered in the hospital (OR 4.5). CONCLUSIONS: Infants who are older, are born full term, have no genitourinary disease, receive shorter courses of IV antibiotics, and do not have a VCUG ordered have shorter stays and may be eligible for a short-stay unit. Earlier transition to oral antibiotics and delayed ordering of a VCUG may decrease LOS.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".