Evidence map›Paper›PMID 35055319›Full record

ArticleJournal of personalized medicine2021

Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data.

Yohwan Yeo, Dong Wook Shin, Jungkwon Lee, Kyungdo Han, Sang Hyun Park, Keun Hye Jeon, Jungeun Shin, Aesun Shin, Jinsung Park

Open access · goldAbstract read
In one paragraph

Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

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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

9 authors at 7 institutions in 1 country.

Yohwan YeoDepartment of Family Medicine and Supportive Care Center, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul 06351, Korea.
Dong Wook ShinDepartment of Family Medicine and Supportive Care Center, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul 06351, Korea.
Jungkwon LeeDepartment of Family Medicine and Supportive Care Center, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul 06351, Korea.
Kyungdo HanDepartment of Statistics and Actuarial Science, Soongsil University, Seoul 06978, Korea.
Sang Hyun ParkDepartment of Medical Statistics, College of Medicine, Catholic University of Korea, Seoul 06951, Korea.
Keun Hye JeonDepartment of Family Medicine, CHA Gumi Medical Center, Gumi 38295, Gyeongsangbuk-do, Korea.
Jungeun ShinDepartment of Family Medicine and Supportive Care Center, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul 06351, Korea.
Aesun ShinDepartment of Preventive Medicine, College of Medicine, Seoul National University, Seoul 03080, Korea.ORCID 0000-0002-6426-1969
Jinsung ParkDepartment of Urology, Uijeongbu Eulji Medical Center, School of Medicine, Eulji University, Uijeongbu-si 11759, Gyeonggi-do, Korea.ORCID 0000-0001-6158-3359
Samsung Medical Center · KRSeoul National University · KRCatholic University of Korea · KREulji University · KRGumi University · KRSamsung (South Korea) · KRSoongsil University · KR

Funding

Korean Foundation for Cancer Research KFCR-2017-C-1
6 · The paper itself

Abstract

Prostate cancer is the fourth most common cause of cancer in men in Korea, and there has been a rapid increase in cases. In the present study, we constructed a risk prediction model for prostate cancer using representative data from Korea. Participants who completed health examinations in 2009, based on the Korean National Health Insurance database, were eligible for the present study. The crude and adjusted risks were explored with backward selection using the Cox proportional hazards model to identify possible risk variables. Risk scores were assigned based on the adjusted hazard ratios, and the standardized points for each risk factor were proportional to the β-coefficient. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability was assessed by plotting the mean predicted probability against the mean observed probability of prostate cancer. Among the candidate predictors, age, smoking intensity, body mass index, regular exercise, presence of type 2 diabetes mellitus, and hypertension were included. Our risk prediction model showed good discrimination (c-statistic: 0.826, 95% confidence interval: 0.821-0.832). The relationship between model predictions and actual prostate cancer development showed good correlation in the calibration plot. Our prediction model for individualized prostate cancer risk in Korean men showed good performance. Using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding prostate cancer screening.

Indexed as

decision aidspersonalized riskpredictionprostate cancer

Identifiers

PMID35055319
PMCPMC8780119
OpenAlexW4200297723

What OpenQuestion 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 OpenQuestion graph.