The Prevalence of Multimorbidity Among Genitourinary Cancer Patients in Chile: A Retrospective Population-Based Study
Bibliographic record
Abstract
Introduction and Objectives: Multimorbidity, defined as the coexistence of two or more chronic conditions, poses significant challenges in healthcare by affecting patient outcomes and increasing costs. This study aimed to evaluate multimorbidity’s impact on patients with genitourinary cancer (GUC) in Chile, focusing on prevalent comorbidities, their combinations, and their association with hospitalization severity. Materials and Methods: A retrospective, population-based study was conducted using data from the Fondo Nacional de Salud (FONASA) in Chile, including patients with bladder, prostate, kidney, and testicular cancer between 2019 and 2021. Diagnosis-related group (DRG) data were used to analyze comorbidity prevalence, hospitalization type (elective vs. emergency), severity (moderate/major vs. minor/none), length of stay, and associated costs. Results: Among 4,028,597 hospital events, 11.6% were related to GUC, involving 18,792 patients. Multimorbidity was present in 67.3% of patients, with hypertension and diabetes being the most common comorbidities. These patients accounted for 69.1% of total GUC care costs. Hospital mortality was higher in multimorbid patients (7.5% vs. 3.7%; p < 0.001), who also had longer stays (mean 8 vs. 5 days). Most patients were admitted electively (60.3%), while 39.7% were admitted through the emergency room. Patients with multimorbidity had higher rates of moderate/major severity hospitalizations compared to those without (56.1% vs. 32.5%; p < 0.001). Conclusions: In Chile, multimorbidity among GUC patients is linked to increased costs, longer hospital stays, higher mortality, and greater hospitalization severity. Comprehensive care strategies are needed to improve outcomes and reduce healthcare system burdens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".