Increasing medical complexity among inpatients in urology over time
Bibliographic record
Abstract
INTRODUCTION: This study aimed to evaluate temporal trends in the medical complexity of urologic inpatients and investigate their implications for clinical care delivery. METHODS: A retrospective comparative chart review was conducted for urologic inpatients admitted to a tertiary care center during two time periods: 2006-2007 and 2019-2020. A random sample of 150 patient charts from each cohort (N=300) was analyzed using a structured data extraction protocol in REDCap. Indicators of medical complexity included comorbidities, polypharmacy, and healthcare resource utilization. Statistical analyses comprised independent-samples t-tests, logistic regression, and multiple linear regression modeling. RESULTS: Analysis of 300 patient records revealed a significant increase in medical complexity in the contemporary cohort compared to the historical cohort. Patients admitted in 2019-2020 exhibited higher Charlson comorbidity index scores, a greater number of chronic conditions, and increased polypharmacy. Utilization of home care services and specialist consultations during hospitalization was also more prevalent in the contemporary cohort. Although length of stay (LOS) remained comparable between cohorts, open abdominal surgery and the number of prescription medications were significant predictors of prolonged LOS (p<0.05). CONCLUSIONS: The medical complexity of urologic inpatients has escalated over time, driven by increased comorbid burden and healthcare system interactions. Despite advances in surgical techniques that would traditionally reduce LOS, these improvements may be counterbalanced by the growing complexity of patient populations. Interventions, such as pre-admission optimization and integrated multidisciplinary care, are essential to address the challenges posed by this evolving clinical landscape.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".