Evidence map›Paper›PMID 37689768›Full record

ArticleScientific reports2023

Comprehensive prediction of urolithiasis based on clinical factors, blood chemistry and urinalysis: UROLITHIASIS score.

Hyo Joon Kim, Sang Hoon Oh

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. 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
1.0field-weighted citation impact, top 21% of its field
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, 4 citations in OpenAlex.

  1. Article
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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

2 authors at 1 institution in 1 country.

Hyo Joon KimDepartment of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06509, Republic of Korea.
Sang Hoon OhDepartment of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06509, Republic of Korea. ohmytweety@catholic.ac.kr.
The Catholic University of Korea Seoul St. Mary's Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Comprehensive prediction of urolithiasis using available factors obtained in the emergency department may aid in patient-centered diagnostic imaging decisions. This retrospective study analyzed the clinical factors, blood chemistry and urine parameters of patients who underwent nonenhanced urinary computed tomography for suspected urolithiasis. A scoring system was developed from a logistic regression model and was tested using the area under the curve (AUC). The prevalence of urolithiasis and important possible causes in the three risk subgroups were determined. Finally, the scoring model was validated. In the derivation cohort (n = 673), 566 patients were diagnosed with urolithiasis. Age > 35 years, history of urolithiasis, pain duration < 8 h, nausea/vomiting, costovertebral angle tenderness, serum creatinine ≥ 0.92 mg/dL, erythrocytes ≥ 10/high power field, no leukocytes ≤  + , and any crystalluria were retained in the final multivariable model and became part of the score. This scoring model demonstrated good discrimination (AUC 0.808 [95% CI, 0.776-0.837]). In the validation cohort (n = 336), the performance was similar (AUC 0.803 [95% CI, 0.756-0.844]), surpassing that of the STONE score (AUC 0.654 [95% CI, 0.601-0.705], P < 0.001). This scoring model successfully stratified patients according to the probability of urolithiasis. Further validation in various settings is needed.

Indexed as

Body FluidsUrolithiasisAdultArea Under CurveHumansRetrospective StudiesUrinalysis

Identifiers

PMID37689768
PMCPMC10492849
OpenAlexW4386569559

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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 OpenQuestion graph.