Evidence map›Paper›PMID 38245498›Full record

Observational studySchizophrenia bulletin2025

Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications.

Dan Zhang, Lihua Xu, Xu Liu, Huiru Cui, Yanyan Wei, Wensi Zheng, Yawen Hong, Zhenying Qian, Yegang Hu, Yingying Tang and 6 more

Open access · bronzeAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Schizophrenia bulletin, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 6% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
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

16 authors at 4 institutions in 4 countries.

Dan ZhangShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.ORCID 0009-0007-8954-1100
Lihua XuShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Xu LiuShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.ORCID 0000-0001-9173-9174
Huiru CuiShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Yanyan WeiShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.ORCID 0000-0002-8218-1954
Wensi ZhengShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Yawen HongShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Zhenying QianShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Yegang HuShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Yingying TangShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Chunbo LiShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.ORCID 0000-0002-3387-4439
Zhi LiuShanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, PR China.
Tao ChenLabor and Worklife Program, Harvard University, Cambridge, MA, USA.
Haichun LiuDepartment of Automation, Shanghai Jiao Tong University, Shanghai, PR China.
Tianhong ZhangShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Jiaotong University School of Medicine, Shanghai, PR China.ORCID 0000-0002-5379-7119
Jijun WangShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Shanghai Jiao Tong University · CNHarvard University · USShanghai Mental Health Center · CNShanghai University · CN

Funding

Clinical Research Plan of SHDC SHDC2022CRD026National Natural Science Foundation of China 82151314
6 · The paper itself

Abstract

background and hypothesisSubstantive inquiry into the predictive power of eye movement (EM) features for clinical high-risk (CHR) conversion and their longitudinal trajectories is currently sparse. This study aimed to investigate the efficiency of machine learning predictive models relying on EM indices and examine the longitudinal alterations of these indices across the temporal continuum. STUDY

designEM assessments (fixation stability, free-viewing, and smooth pursuit tasks) were performed on 140 CHR and 98 healthy control participants at baseline, followed by a 1-year longitudinal observational study. We adopted Cox regression analysis and constructed random forest prediction models. We also employed linear mixed-effects models (LMMs) to analyze longitudinal changes of indices while stratifying by group and time. STUDY

resultsOf the 123 CHR participants who underwent a 1-year clinical follow-up, 25 progressed to full-blown psychosis, while 98 remained non-converters. Compared with the non-converters, the converters exhibited prolonged fixation durations, decreased saccade amplitudes during the free-viewing task; larger saccades, and reduced velocity gain during the smooth pursuit task. Furthermore, based on 4 baseline EM measures, a random forest model classified converters and non-converters with an accuracy of 0.776 (95% CI: 0.633, 0.882). Finally, LMMs demonstrated no significant longitudinal alterations in the aforementioned indices among converters after 1 year.

conclusionsAberrant EMs may precede psychosis onset and remain stable after 1 year, and applying eye-tracking technology combined with a modeling approach could potentially aid in predicting CHRs evolution into overt psychosis.

Indexed as

Disease ProgressionEye Movement MeasurementsEye MovementsMachine LearningProdromal SymptomsPsychotic DisordersPursuit, SmoothSchizophreniaAdolescentAdultFemaleFollow-Up StudiesHumansLongitudinal StudiesMaleYoung Adultclinical high risk for psychosisearly psychosiseye movementlongitudinal modelingtransition

Identifiers

PMID38245498
PMCPMC11908855
OpenAlexW4391057179

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.