Evidence map›Paper›PMID 40067913›Full record

ArticleUrology practice2025

Use and Usefulness of Risk Prediction Tools in Urologic Surgery: Current State and Path Forward.

Elizabeth M Nazzal, Allison M Deal, Benjamin Borgert, Hillary Heiling, Antonia V Bennett, Susan Blalock, William Meeks, Raymond Fang, Randall Teal, Maihan B Vu and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Urology practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Elizabeth M NazzalDepartment of Urology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
Allison M DealLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
Benjamin BorgertDepartment of Urology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
Hillary HeilingLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
Antonia V BennettDepartment of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina.
Susan BlalockPharmaceutical Outcomes & Policy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina.
William MeeksAmerican Urological Association Data and Statistical Services, Linthicum, Maryland.
Raymond FangAmerican Urological Association Data and Statistical Services, Linthicum, Maryland.
Randall TealLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
Maihan B VuLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
David GotzLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
Matthew E NielsenDepartment of Urology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
Alex H S HarrisDepartment of Surgery, School of Medicine, Stanford University, Palo Alto, California.
Ethan BaschLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
Hung-Jui TanDepartment of Urology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.

Funding

Virology Research Program (Program 4)P30CA016086 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Deborah F. Tate · 1985 to 2026
$201.5M
NCI NIH HHS P30 CA016086NIA NIH HHS L30 AG051183
6 · The paper itself

Abstract

introductionAlthough the enthusiasm for artificial intelligence (AI) to enhance surgical decision-making continues to grow, the preceding advance of risk prediction tools (RPTs) has had limited impact to date. To help inform the development of AI-powered tools, we evaluated the role of RPTs and prevailing attitudes among urologists.

methodsWe conducted a national mixed methods study using a sequential explanatory design. Through the 2019 AUA Census, we surveyed urologists on RPT use, helpfulness, and trust. Based on responses, we interviewed 25 participants on RPTs, risk evaluation, and surgical decision-making. Coding-based thematic analysis was applied and integrated with survey findings.

resultsAmong 2081 urologic surgeons (weighted sample 12,366), 30.4% (95% CI, 28.2%-32.6%) routinely used RPTs and 34.3% (95% CI, 31.9%-36.6%) found them helpful while 47.0% (95% CI, 44.6%-49.5%) generally trusted their own assessment over RPT-generated estimates. More years in practice was negatively associated with RPT use, helpfulness, and trust (

conclusionsDespite their wide availability, RPTs remain limited in their use and helpfulness. This reflects both the intuitive nature of surgical decision-making and implementation challenges. For AI to reach its promise and improve surgical care and outcomes, both types of barriers will need to be addressed.

Indexed as

Artificial IntelligenceClinical Decision-MakingUrologic Surgical ProceduresUrologistsAdultAttitude of Health PersonnelFemaleHumansMaleMiddle AgedRisk AssessmentSurveys and Questionnairespredictive toolsusabilityuser experience

Identifiers

PMID40067913
PMCPMC12187546

What Socratic holds

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Registered trials

None linked

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.