ArticleFrontiers in cellular and infection microbiology2026
Predictors for identifying autoimmune encephalitis in pediatric patients.
Article in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study aimed to identify the independent predictors and develop a predictive model for autoimmune encephalitis (AE) in pediatric populations. Methods: This retrospective study comprised 88 pediatric patients with encephalitis (37 AE cases and 51 non- AE cases) at Children's Hospital Affiliated to Shandong University between May 2020 and April 2025. Lasso regression analysis, univariate and multivariate logistic analysis was used to identify autoimmune encephalitis associated risk factors. The nomogram visualized the results. Receiver operating characteristic (ROC) curves, calibration plots, Brier scoring and decision curve analysis (DCA) were used to evaluate the diagnostic model. Results: 16 clinical variables significantly differed between the autoimmune encephalitis and non-autoimmune encephalitis groups. Lasso regression analysis, univariate and multivariate logistic analysis identified four significant independent predictors: age (OR: 1.44; 95% CI: 1.09-1.91; Conclusions: This study established and validated a high-performance predictive model incorporating four clinically accessible parameters for the diagnosis of pediatric autoimmune encephalitis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.