Evidence map›Paper›PMID 31027130›Full record

ArticleMedicine2019

Effects of different interventions on animal models of ischemic stroke: Protocol for an overview and a network meta-analysis.

Yan Zhang, Hui-Jin Yu, Shu-Zhen Shi, Jian-Cheng Wang

Abstract read
In one paragraph

Article in Medicine, 2019. 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

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

2 citing papers in PubMed.

  1. Article
  2. Article
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

4 authors.

Yan ZhangSpinal Cord Injury Rehabilitation Department, Rehabilitation Center Hospital of Gansu Province.
Hui-Jin YuSpinal Cord Injury Rehabilitation Department, Rehabilitation Center Hospital of Gansu Province.
Shu-Zhen ShiSchool of Basic Medical Sciences, Lanzhou University.
Jian-Cheng WangGansu Provincial Hospital, Lanzhou City, Gansu Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIschemic stroke often leads to lifelong disability or death in stroke patients. It is one of the most common causes of death and disability worldwide, so it is a global health problem. The objective of this protocol is to provide the methods for using overview and network meta-analysis to identify the more effective intervention for infarct volume and neurobehavioral score in animal models of ischemic stroke.

methodsA systematic literature search will be conducted in PubMed and Embase to obtain relevant systematic reviews on December 11, 2018. Assessing the Methodological Quality of Systematic Reviews (AMSTAR2) and SYRCLE's risk of bias tool will be used to assess quality of the included reviews and risk of bias of randomized controlled trials (RCTs) for animal studies. Infarct volume and neurobehavioral score will be chosen as primary and secondary outcomes. The relative effect size of the treatment will be calculated using the standardized mean difference (SMD) and 95% confidence interval (CI). R 3.5.1 through the GEMTC package will be used to perform a network meta-analysis to synthesize direct and indirect evidence.

resultsThe results of this paper will be submitted to a peer-reviewed journal for publication.

conclusionOur study can provide a reference for further clinical practice and can be compared with clinical trial results to obtain a more credible therapeutic effect of this intervention. ETHICS AND COMMUNICATION: Formal ethical approval is unnecessary, because this study is based on published researches. PROSPERO REGISTRATION NUMBER: CRD42019126811.

Indexed as

AnimalsBrain IschemiaDisease Models, AnimalNetwork Meta-Analysis as TopicStrokeSystematic Reviews as Topic

Identifiers

PMID31027130
PMCPMC6831226

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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 OpenQuestion graph.