Evidence map›Paper›PMID 42412135›Full record

ArticleIrish journal of medical science2026

A predictive nomogram for relapse risk in children with autoimmune encephalitis.

Wen-Ting Yao, Zhen Li

Abstract read
PubMed Publisher
In one paragraph

Article in Irish journal of medical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Wen-Ting YaoDepartment of Neurology, Hangzhou Children's Hospital, No. 195 Wenhui Road, Gongshu District, Hangzhou, Zhejiang, 310005, P.R. China. Yaowenting1993@protonmail.com.
Zhen LiDepartment of Emergency, Hangzhou Children's Hospital, Hangzhou, 310005, Zhejiang, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAutoimmune encephalitis (AE) is a major cause of acute encephalopathy in children, and relapse is strongly linked to long-term neurodevelopmental sequelae. However, studies on relapse risk prediction in the Chinese pediatric population remain limited, and validated tools for individualized risk stratification are lacking. This study aimed to develop and validate a nomogram for predicting relapse risk in children with AE.

methodsThis retrospective cohort study included 127 children aged 1-16 years diagnosed with AE at a tertiary pediatric center in China between 2017 and 2023. Independent predictors of relapse were identified using univariable and multivariable logistic regression. A nomogram was constructed and comprehensively validated for discrimination, calibration, and stability.

resultsThe overall relapse rate was 26.8% (34/127). Independent risk factors for relapse included age ≥ 8 years at onset (OR 3.19, 95% CI 1.37-7.42, P 0.007), limb weakness (OR 2.36, 95% CI 1.03-5.42, P 0.041), delayed immunotherapy (OR 2.77, 95% CI 1.23-6.24, P 0.013), and positive N-methyl-D-aspartate receptor (NMDAR) antibody (OR 2.91, 95% CI 1.29-6.56, P 0.010). Rituximab treatment (OR 0.43, 95%CI 0.19-0.95, P 0.036) and IVIG treatment ≥ 6 months (OR 0.35, 95%CI 0.13-0.92, P 0.033) were confirmed as independent protective factors. The nomogram showed excellent discriminative ability (C-statistic 0.936, R² 0.683), good calibration (Brier score 0.081, calibration intercept < 0.001, slope 1.000), and robust stability on internal validation with 1000 bootstrap resamples.

conclusionThis nomogram demonstrates favorable accuracy and clinical utility for individualized relapse risk prediction in Chinese children with AE.

Indexed as

ImmunotherapyNomogramPediatric autoimmune encephalitisPredictive modelRelapseRisk factors

Identifiers

PMID42412135

What OpenQuestion holds

Textmetadata
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.