Evidence map›Paper›PMID 38841696›Full record

ArticleFrontiers in neurology2024

Serum neurofilament light chain: a predictive marker for outcomes following mild-to-moderate ischemic stroke.

Chongxi Xu, Tong Yi, Ting Qing, Yongliang Jiang, Xingyang Yi, Jianguo Xu, Junpeng Ma

Abstract read
In one paragraph

Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

7 authors.

Chongxi Xu *Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Tong Yi *Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Ting QingDepartment of Neurology, The Second People's Hospital of Deyang City, Deyang, Sichuan, China.
Yongliang JiangDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Xingyang YiDepartment of Neurology, People's Hospital of Deyang City, Deyang, Sichuan, China.
Jianguo XuDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Junpeng MaDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Biomarkers that reflect brain damage or predict functional outcomes may aid in guiding personalized stroke treatments. Serum neurofilament light chain (sNfL) emerges as a promising candidate for fulfilling this role. Methods: This prospective, observational cohort investigation included 319 acute ischemic stroke (IS) patients. The endpoints were the incidence of early neurological deterioration (END, an elevation of two or more points in the National Institute of Health stroke scale score within a week of hospitalization compared with the baseline) and functional outcome at 3 months (an mRS score of >2 at 3 months was categorized as an unfavorable/poor functional outcome). The association of sNfL, which was assessed within 24 h of admission, with END and unfavorable functional outcomes at follow-up was assessed via multivariate logistic regression, whereas the predictive value of sNfL for unfavorable functional outcomes and END was elucidated by the receiver operating characteristic curve (ROC). Results: Of 319 IS individuals, 89 (27.90%) suffered from END. sNfL not only reflects the severity of stroke measured by NIHSS score ( Conclusion: This investigation suggests that sNfL can serve as a valuable biomarker for predicting END and 3-month poor functional outcomes after an IS and has the potential to forecast long-term cardiovascular outcomes.

Indexed as

cardiovascular eventsearly neurological deteriorationfunctional outcomesischemic strokeserum neurofilament light chain protein

Identifiers

PMID38841696
PMCPMC11150679

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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.