Evidence map›Paper›PMID 41385296›Full record

ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2026

Diagnostic value of saccades in mild cognitive impairment: a community-based study.

Leihao Sha, Hua Li, Anjiao Peng, Huajun Yang, Xin Liu, Hongjian Zhao, Wenbo Ma, Qiulei Hong, Yusha Tang, Mingsha Zhang and 1 more

Abstract read
In one paragraph

Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

11 authors.

Leihao ShaDepartment of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Hua LiDepartment of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Anjiao PengDepartment of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Huajun YangDepartment of Neurology, Affiliated Hospital of Chengdu University, Chengdu, Sichuan, China.
Xin LiuDepartment of Neurology, Affiliated Hospital of Chengdu University, Chengdu, Sichuan, China.
Hongjian ZhaoDepartment of Neurology, Affiliated Hospital of Chengdu University, Chengdu, Sichuan, China.
Wenbo MaSchool of Psychiatry, North Sichuan Medical College, Nanchong, Sichuan, China.
Qiulei HongDepartment of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Yusha TangDepartment of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Mingsha ZhangState Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Division of Psychology, Beijing Normal University, Beijing, China.ORCID 0000-0002-5407-7770
Lei ChenDepartment of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.ORCID 0000-0001-5263-5540

Funding

Science and Technology Department of Sichuan Province 24NSFSC3242
6 · The paper itself

Abstract

backgroundAccurate diagnosis and assessment of mild cognitive impairment (MCI) are essential. The efficacy of saccades in the detection of MCI lacks validation through large-scale clinical trials.

methodsAll eligible participants underwent saccadic assessment in four tasks and a cognitive assessment. MCI diagnoses were made on the basis of clinical indicators and MRI by experienced physicians. The physicians were blinded to the saccade experiments, and the operators of the saccade experiments were blind to the diagnosis of physicians. The classification models based on machine learning were constructed for assessing the diagnostic accuracy of MCI based on saccadic parameters.

resultsOf the 559 residents who consented to participate, 383 (153 with MCI and 230 controls) were completely assessed. The classification model trained by saccadic parameters achieved high accuracy in dissociating MCI and control with an area under the curve (AUROC) of 0.945 (95% CI, 0.924-0.964), sensitivity of 0.824 (95% CI, 0.769-0.886) and specificity of 0.904 (95% CI, 0.867-0.935). The parameters of the memory-guided and antisaccade tasks demonstrated better diagnostic efficacy. The saccade model also exhibited a good diagnostic value in patients with borderline cognition, being defined by the score of the Montreal Cognitive Assessment (MoCA). When the borderline cognition was defined as 23-27 of the MoCA score, the diagnosing accuracy of mild cognitive impairment based on saccadic parameters resulted in AUROC of 0.911 (95% CI, 0.836-0.972), sensitivity of 0.929 (95% CI, 0.762-1.000) and specificity of 0.796 (95% CI, 0.718-0.863).

conclusionsSaccades can distinguish MCI from controls with great accuracy, offering a sensitive and objective diagnostic aid of MCI, especially in participants with borderline cognition.

Indexed as

Cognitive DysfunctionSaccadesAgedAged, 80 and overCase-Control StudiesFemaleHumansMachine LearningMagnetic Resonance ImagingMaleNeuropsychological TestsSensitivity and SpecificityAlzheimer’s diseaseCognitive impairmentEye movementMachine learningOculomotor

Identifiers

PMID41385296
PMCPMC12832980

What Socratic holds

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Registered trials

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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.