ReviewCurrent urology reports2026
Big Data Analytics in Urologic Oncology: A Comprehensive Review of Large-scale Database Research and Clinical Applications.
Review in Current urology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewLarge-scale databases have become an essential resource in urologic oncology, enabling the generation of real-world evidence across broad and diverse patient populations. This narrative review aims to summarize the contribution of population-based cancer registries, hospital-based clinical registries, and administrative healthcare databases to the understanding and management of prostate, bladder, renal, and other genitourinary malignancies. RECENT
findingsOver the past decade, large database studies have provided key insights into cancer epidemiology, treatment patterns, outcomes, and healthcare disparities. These data sources complement randomized clinical trials by capturing routine clinical practice at a population level. Major findings include shifts in cancer incidence and mortality, increased adoption of active surveillance in low-risk prostate cancer, and greater use of nephron-sparing approaches in renal cell carcinoma. In addition, large-scale analyses have highlighted disparities related to patient demographics, institutional volume, and access to care. They have also contributed to evaluating the real-world effectiveness and safety of established therapies, particularly in populations often underrepresented in clinical trials. Despite their strengths-particularly large sample sizes and enhanced generalizability-big data studies remain subject to important limitations, including residual confounding, coding variability, and limited availability of granular clinical and biological information. When interpreted with appropriate methodological rigor, these observational data provide valuable evidence to support clinical decision-making, inform guideline development, and identify gaps in care. Continued improvements in data quality, analytical methods, and integration with emerging technologies are expected to further strengthen the role of real-world data in advancing urologic oncology.
Indexed as
Identifiers
42579222What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.