Evidence map›Paper›PMID 41680387›Full record

ReviewNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2026

Risk-predicting and screening tools of post-stroke delirium: a narrative review.

Xiaoyan Cai, Yuxuan Zhao, Li-E Lin, Chunli Huang, Fengxia Yan, Haiqun Huang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 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

6 authors.

Xiaoyan CaiDepartment of Nursing, Foshan University Hospital of Stomatology, Foshan Stomatological Hospital, Foshan, Guangdong, 528000, China.ORCID http://orcid.org/0000-0001-7423-430X
Yuxuan ZhaoDepartment of Nursing, Foshan University Hospital of Stomatology, Foshan Stomatological Hospital, Foshan, Guangdong, 528000, China.
Li-E LinDepartment of Nursing, Foshan University Hospital of Stomatology, Foshan Stomatological Hospital, Foshan, Guangdong, 528000, China.
Chunli HuangGlobal Dental Care Center, VIP Comprehensive Dental Clinic, Foshan University Hospital of Stomatology, Foshan Stomatological Hospital, Foshan, Guangdong, 528000, China.
Fengxia YanSchool of Nursing, Jinan University, Guangzhou, Guangdong, 510632, China. yanfengxia0807@163.com.
Haiqun HuangDepartment of Gastroenterology, Guangzhou First People's Hospital, Guangzhou, Guangdong, China. hhq13723584129@163.com.ORCID http://orcid.org/0000-0002-7005-8967

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Post-stroke delirium (PSD) is a common yet frequently under-recognized complication in clinical practice. This review provides a comprehensive mapping of the rapidly evolving landscape of PSD risk-predicting and screening tools. The distinctive strengths and limitations of each tool were synthesized, highlighting what they effectively capture and what they may overlook. A key trade-off emerges: highly sensitive instruments often lack feasibility in busy stroke units, whereas shorter tools may suffer from cultural and contextual biases. Emerging context-adaptive and language-agnostic tools show particular promise in transforming delirium from an overlooked barrier to recovery into a manageable and treatable target after stroke.

Indexed as

DeliriumStrokeHumansPost-stroke deliriumReviewRisk-predictingScreening

Identifiers

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.