Evidence map›Paper›PMID 40927768›Full record

ArticleNeurotrauma reports2025

Identifying Features of Electroencephalography Associated with Improved Awareness in Persistent Vegetative State via Multiscale Entropy: A Machine Learning Modeling Study.

Keyun Lai, Xiao Chen, Liyun He, Qi Liu, Changsheng Lai, Yang Bai, Ye Zhang, Kaiyue Wang, Fangzhao Wang, Shuai He and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurotrauma reports, 2025. 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

11 authors.

Keyun LaiXi'an Traditional Chinese Medicine Encephalopathy Hospital Affiliated to Shaanxi University of Traditional Chinese Medicine, Xi'an, China.
Xiao ChenXi'an Traditional Chinese Medicine Encephalopathy Hospital Affiliated to Shaanxi University of Traditional Chinese Medicine, Xi'an, China.
Liyun HeInstitute of Clinical Basic Medicine of Traditional Chinese Medicine, Chinese Academy of Chinese Medical Sciences, Beijing, China.
Qi LiuInstitute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing, China.
Changsheng LaiPreventive Health Section, Yulin Red Cross Hospital, Yulin, China.
Yang BaiXi'an Traditional Chinese Medicine Encephalopathy Hospital Affiliated to Shaanxi University of Traditional Chinese Medicine, Xi'an, China.
Ye ZhangXi'an Traditional Chinese Medicine Encephalopathy Hospital Affiliated to Shaanxi University of Traditional Chinese Medicine, Xi'an, China.
Kaiyue WangXi'an Traditional Chinese Medicine Encephalopathy Hospital Affiliated to Shaanxi University of Traditional Chinese Medicine, Xi'an, China.
Fangzhao WangXi'an Traditional Chinese Medicine Encephalopathy Hospital Affiliated to Shaanxi University of Traditional Chinese Medicine, Xi'an, China.
Shuai HeXi'an Traditional Chinese Medicine Encephalopathy Hospital Affiliated to Shaanxi University of Traditional Chinese Medicine, Xi'an, China.
Guangjun WangInstitute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate differentiation between persistent vegetative state (PVS) and minimally conscious state and estimation of recovery likelihood in patients in PVS are crucial. This study analyzed electroencephalography (EEG) metrics to investigate their relationship with consciousness improvements in patients in PVS and developed a machine learning prediction model. We retrospectively evaluated 19 patients in PVS, categorizing them into two groups: those with improved consciousness (

Indexed as

electroencephalogrammachine learningmultiscale entropypersistent vegetative stateprediction

Identifiers

PMID40927768
PMCPMC12416531

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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.