Evidence map›Paper›PMID 41094652›Full record

ArticleBMC psychology2025

Construction and verification of nomogram prediction model for non-suicidal self-injury in adolescents with depression.

Yuehong Gao, Yun Chen, Jiajia Shi, Xiaoli Mao, Jinhong Wang, Jialu He, Hongmei Huang, Xujuan Xu

Abstract read
In one paragraph

Article in BMC psychology, 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

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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

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

8 authors.

Yuehong GaoThe Fourth People's Hospital of Nantong, Jangsu, China.
Yun ChenThe Fourth People's Hospital of Nantong, Jangsu, China.
Jiajia ShiThe Fourth People's Hospital of Nantong, Jangsu, China.
Xiaoli MaoThe Fourth People's Hospital of Nantong, Jangsu, China.
Jinhong WangThe Fourth People's Hospital of Nantong, Jangsu, China.
Jialu HeThe Fourth People's Hospital of Nantong, Jangsu, China.
Hongmei HuangThe Fourth People's Hospital of Nantong, Jangsu, China. 913944372@qq.com.
Xujuan XuNursing Research Institute, Affiliated Hospital of Nantong University, Jangsu, China. 2205434494@qq.com.

Funding

Nantong City Social Livelihood Science and Technology project MS2023023Nantong Municipal Health and Wellness Committee MS2022080
6 · The paper itself

Abstract

backgroundAccurate identification of high-risk individuals for NSSI and timely intervention are critical for mitigating self-harm risk. This study aimed to develop a predictive model for NSSI behaviors in adolescents with depression.

methodsA convenience sample of 596 adolescents with depression was assessed, with 455 assigned to the training and internal validation set and 144 to the external validation set. Nine key predictors were identified through univariate analysis, LASSO regression, and binary logistic regression, including birth mode, history of peer self-harm, parental psychiatric disorders, sleep duration, social life events, self-esteem, psychological resilience, social support, and depression severity. A nomogram-based prediction model was constructed from these factors, with model performance evaluated via ROC curves, AUC values, Hosmer-Lemeshow test, and calibration curves. Clinical applicability was determined using decision curve analysis (DCA).

resultsThe model exhibited an AUC of 0.880 (P < 0.001), with sensitivity of 0.933 and specificity of 0.765. The Hosmer-Lemeshow test confirmed good model fit (χ

conclusionThe nomogram-based risk model developed in this study effectively predicts NSSI behaviors in adolescents with depression, offering significant scientific and clinical value and warranting further implementation.

Indexed as

DepressionNomogramsSelf-Injurious BehaviorAdolescentFemaleHumansMaleRisk FactorsROC CurveSelf ConceptAdolescent depressionModelNomogramNon-suicidal self-injury (NSSI)Prediction

Identifiers

PMID41094652
PMCPMC12522271

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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.