Evidence mapPaperPMID 33303841Full record

ArticleScientific reports2020

A prediction nomogram for the 3-year risk of incident diabetes among Chinese adults.

Yang Wu, Haofei Hu, Jinlin Cai, Runtian Chen, Xin Zuo, Heng Cheng, Dewen Yan

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  12. A Nomogram for Predicting Prognosis of AdvancedTropical medicine and infectious disease · 2023
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  20. Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches.International journal of environmental research and public health · 2021
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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

7 authors.

Yang WuDepartment of Endocrinology, The First Affiliated Hospital of Shenzhen University, No.3002 Sungang Road, Futian District, Shenzhen, 518035, Guangdong Province, China.
Haofei HuDepartment of Nephrology, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong Province, China.
Jinlin CaiDepartment of Endocrinology, The First Affiliated Hospital of Shenzhen University, No.3002 Sungang Road, Futian District, Shenzhen, 518035, Guangdong Province, China.
Runtian ChenDepartment of Endocrinology, The First Affiliated Hospital of Shenzhen University, No.3002 Sungang Road, Futian District, Shenzhen, 518035, Guangdong Province, China.
Xin ZuoDepartment of Endocrinology, Shenzhen Third People's Hospital, Shenzhen, 518116, Guangdong Province, China.
Heng ChengDepartment of Endocrinology, Shenzhen Third People's Hospital, Shenzhen, 518116, Guangdong Province, China.
Dewen YanDepartment of Endocrinology, The First Affiliated Hospital of Shenzhen University, No.3002 Sungang Road, Futian District, Shenzhen, 518035, Guangdong Province, China. yandw963@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying individuals at high risk for incident diabetes could help achieve targeted delivery of interventional programs. We aimed to develop a personalized diabetes prediction nomogram for the 3-year risk of diabetes among Chinese adults. This retrospective cohort study was among 32,312 participants without diabetes at baseline. All participants were randomly stratified into training cohort (n = 16,219) and validation cohort (n = 16,093). The least absolute shrinkage and selection operator model was used to construct a nomogram and draw a formula for diabetes probability. 500 bootstraps performed the receiver operating characteristic (ROC) curve and decision curve analysis resamples to assess the nomogram's determination and clinical use, respectively. 155 and 141 participants developed diabetes in the training and validation cohort, respectively. The area under curve (AUC) of the nomogram was 0.9125 (95% CI, 0.8887-0.9364) and 0.9030 (95% CI, 0.8747-0.9313) for the training and validation cohort, respectively. We used 12,545 Japanese participants for external validation, its AUC was 0.8488 (95% CI, 0.8126-0.8850). The internal and external validation showed our nomogram had excellent prediction performance. In conclusion, we developed and validated a personalized prediction nomogram for 3-year risk of incident diabetes among Chinese adults, identifying individuals at high risk of developing diabetes.

Indexed as

NomogramsAdultAsian PeopleChinaDiabetes MellitusFemaleHumansIncidenceMalePredictive Value of TestsRetrospective StudiesRiskROC CurveTime Factors

Identifiers

PMID33303841
PMCPMC7729957

What Socratic holds

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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 Socratic graph.