Evidence map›Paper›PMID 36412699›Full record

ArticleNeurology international2022

The Utility of Automated ASPECTS in Acute Ischemic Stroke for Intravenous Recombinant Tissue Plasminogen Activator (IV-rtPA) Therapy.

Soichiro Shibata, Kenzo Sakurai, Keiji Tachikawa, Riyoko Ko, Sakae Hino, Takayuki Fukano, Kenji Isahaya, Takafumi Haraguchi, Junji Yamauchi, Kenichiro Tanabe and 7 more

Abstract read
In one paragraph

Article in Neurology international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

17 authors.

Soichiro ShibataDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.ORCID 0000-0001-6687-8068
Kenzo SakuraiDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Keiji TachikawaDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Riyoko KoDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Sakae HinoDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Takayuki FukanoDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Kenji IsahayaDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Takafumi HaraguchiDepartment of Advanced Biomedical Imaging and Informatics, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Junji YamauchiDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Kenichiro TanabeDepartment of Frontier Medicine, Institute of Medical Science, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.ORCID 0000-0001-6271-2490
Misako NagasakaDivision of Hematology and Oncology, Department of Medicine, University of California, Irvine School of Medicine, Orange, CA 92697, USA.
Yuta HagiwaraDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Takahiro ShimizuDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Hisanao AkiyamaDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Yasuyuki KobayashiDepartment of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.
Yasuhiro HasegawaDivision of Neurology, Department of Internal Medicine, Shin-Yurigaoka General Hospital, Kawasaki 215-0026, Japan.ORCID 0000-0003-3200-376X
Yoshihisa YamanoDivision of Neurology, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki 216-8511, Japan.

Funding

Japan Agency for Medical Research and Development No. JP22ek0109529h, JP22ek0109441h, JP22ek0109493s, JP22ek0109548sJapan Society for the Promotion of Science (JSPS) KAKENHI No. JP22H02987Rare and Intractable Diseases from the Ministry of Health, Labour and Welfare of Japan No. JPMH22FC1013
6 · The paper itself

Abstract

purposeThis study aimed to investigate the accuracy and clinical significance of an artificial intelligence (AI)-based automated Alberta Stroke Program Early Computed Tomography (ASPECT) scoring software of head CT for the indication of intravenous recombinant tissue plasminogen activator (rt-PA) therapy.

methodsThis study included two populations of acute ischemic stroke: one comprised patients who had undergone head CT within 48 h of presentation (Population #1,

resultsThe reading accuracy of AI software was comparable to that of the board-certified vascular neurologists. The detection rate of cardiogenic cerebral embolism was better than that of atherothrombotic cerebral infarction. By excluding extensive infarction, AI-software showed a higher specificity and equivalent sensitivity compared to those of experts.

conclusionsThe AI software for ASPECTS showed convincing agreement with expert evaluation and would be supportive in determining the indications of intravenous rt-PA therapy.

Indexed as

acute cerebral infarctionartificial intelligenceASPECTSrecombinant tissue plasminogen activator therapy

Identifiers

PMID36412699
PMCPMC9680239

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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.