Evidence map›Paper›PMID 40565784›Full record

ArticleJournal of clinical medicine2025

AI-Driven Prediction of Renal Stone Recurrence Following ECIRS: A Machine Learning Approach to Postoperative Risk Stratification Incorporating 24-Hour Urine Data.

Takahiro Yanase, Rei Unno, Theodoros Tokas, Vineet Gauhar, Yuya Sasaki, Kengo Kawase, Ryosuke Chaya, Shuzo Hamamoto, Mihoko Maruyama, Takahiro Yasui and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Takahiro YanaseDepartment of Nephro-urology, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan.ORCID 0000-0002-8916-0806
Rei UnnoDepartment of Nephro-urology, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan.ORCID 0000-0003-0584-3712
Theodoros TokasDepartment of Urology, University General Hospital of Heraklion, Medical School, University of Crete, 71500 Heraklion, Greece.ORCID 0000-0003-0928-0507
Vineet GauharNg Teng Fong General Hospital, (NUHS), Singapore 609606, Singapore.ORCID 0000-0002-3740-7141
Yuya SasakiGraduate School of Information Science and Technology, University of Osaka, Osaka 565-0871, Japan.
Kengo KawaseDepartment of Nephro-urology, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan.ORCID 0000-0003-4176-9089
Ryosuke ChayaDepartment of Nephro-urology, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan.
Shuzo HamamotoDepartment of Nephro-urology, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan.ORCID 0000-0002-9968-7468
Mihoko MaruyamaGraduate School of Engineering, University of Osaka, Suita, Osaka 565-0871, Japan.
Takahiro YasuiDepartment of Nephro-urology, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan.ORCID 0000-0003-2197-2477
Kazumi TaguchiDepartment of Nephro-urology, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

24 h urine collectionartificial intelligenceendoscopic combined intrarenal surgery (ECIRS)kidney stone recurrencemachine learning

Identifiers

PMID40565784
PMCPMC12193965

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.