The rising burden of acute urologic disease at an urban, academic hospital network
Bibliographic record
Abstract
INTRODUCTION: Urological presentations to the emergency department (ED) can represent a significant burden of disease. We aimed to evaluate trends in the incidence, management, and followup of urological presentations to the ED at an urban, academic, tertiarycare hospital network over a 10-year period. METHODS: A retrospective cohort study was conducted to include all patients presenting with renal colic (RC), gross hematuria (GH), or acute urinary retention (AUR) to EDs in the University Health Network in 2008-2009 and 2018-2019. Patient demographics and outcomes were compared between these two periods and between urological presentations. Multilevel regression analyses identified predictors of in-patient admission, return to the ED, and clinic wait time. RESULTS: A total of 2751 patients and 3510 ED visits were included (991 visits from 2008-09 and 2519 visits from 2018-19). Over time, increases were observed in all three presentations, largely driven by an almost five-fold increase in RC presentations. Multilevel regression analyses showed that older patients were more likely to be admitted from the ED, while age, 2018-19 era, and residence within the "downtown core" independently predicted return to the ED within 30 days of initial visit. Time to be seen in urology clinic increased over time for the entire cohort, and 14.4% of clinic visits were preceded by multiple ED visits. CONCLUSIONS: The incidence of acute urological presentations increased significantly over a 10-year period at a tertiary-care hospital network. These findings demonstrate an increasing burden of acute urological disease that is outpacing population growth and straining available resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".