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Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment.
Open accessAbstract
The etiology of ischemic stroke affects prognosis, outcome, and management. Trials of therapies for patients with acute stroke should include measurements of responses as influenced by subtype of ischemic stroke. A system for categorization of subtypes of ischemic stroke mainly based on etiology has been developed for the Trial of Org 10172 in Acute Stroke Treatment (TOAST). A classification of subtypes was prepared using clinical features and the results of ancillary diagnostic studies. "Possible" and "probable" diagnoses can be made based on the physician's certainty of diagnosis. The usefulness and interrater agreement of the classification were tested by two neurologists who had not participated in the writing of the criteria. The neurologists independently used the TOAST classification system in their bedside evaluation of 20 patients, first based only on clinical features and then after reviewing the results of diagnostic tests. The TOAST classification denotes five subtypes of ischemic stroke: 1) large-artery atherosclerosis, 2) cardioembolism, 3) small-vessel occlusion, 4) stroke of other determined etiology, and 5) stroke of undetermined etiology. Using this rating system, interphysician agreement was very high. The two physicians disagreed in only one patient. They were both able to reach a specific etiologic diagnosis in 11 patients, whereas the cause of stroke was not determined in nine. The TOAST stroke subtype classification system is easy to use and has good interobserver agreement. This system should allow investigators to report responses to treatment among important subgroups of patients with ischemic stroke. Clinical trials testing treatments for acute ischemic stroke should include similar methods to diagnose subtypes of stroke.
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Traylor, M., Adib‐Samii, P., Harold, D., the Alzheimer's Disease Neuroimaging Initiative, T. I. S. G. C. (., Dichgans, M., Williams, J., Lewis, C. M., Markus, H. S., & and the METASTROKE and International Genomics of Alzheimer's Project (IGAP) investigators (2016) · Annals of Neurology · doi:10.1002/ana.24621 · Cited by 71
Coffee and acute ischemic stroke onset: The Stroke Onset Study
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Early Neurological Change After Ischemic Stroke Is Associated With 90-Day Outcome
Heitsch, L., Ibanez, L., Carrera, C., Binkley, M. M., Strbian, D., Tatlisumak, T., Bustamante, A., Ribó, M., Molina, C., Dávalos, A., et al. (2021) · Stroke · doi:10.1161/strokeaha.119.028687 · Cited by 70
Smoking cessation 1 year poststroke and damage to the insular cortex
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The Reveal LINQ insertable cardiac monitor
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Movement Kinematics of the Ipsilesional Upper Extremity in Persons With Moderate or Mild Stroke
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Fibroblast Growth Factor 23 and Risk of Incident Stroke in Community-Living Adults
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Worse Outcome after Stroke in Patients with Obstructive Sleep Apnea: An Observational Cohort Study
Mansukhani, M. P., Bellolio, M. F., Kolla, B. P., Enduri, S., Somers, V. K., & Stead, L. G. (2011) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2010.02.011 · Cited by 63
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Gebruers, N., Truijen, S., Engelborghs, S., Nagels, G., Brouns, R., & De Deyn, P. P. (2008) · Cerebrovascular Diseases · doi:10.1159/000160210 · Cited by 61
Flexible wireless powered drug delivery system for targeted administration on cerebral cortex
Sung, S. H., Kim, Y. S., Joe, D. J., Mun, B. H., You, B. K., Keum, D. H., Hahn, S. K., Berggren, M., Kim, D., & Lee, K. J. (2018) · Nano Energy · doi:10.1016/j.nanoen.2018.06.015 · Cited by 55
Plasma β-Amyloid 1-40 Is Associated With the Diffuse Small Vessel Disease Subtype
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Changes of brachial flow-mediated vasodilation in different ischemic stroke subtypes
Chen, P. L., Wang, P. Y., Sheu, W. H., Chen, Y. T., Ho, Y. P., Hu, H. H., & Hsu, H. Y. (2006) · Neurology · doi:10.1212/01.wnl.0000237526.32692.67 · Cited by 50
Interleukin-10 Level Is Associated with Post-Stroke Depression in Acute Ischaemic Stroke Patients
Chi, C.-H., Huang, Y.-Y., Ye, S.-Z., Shao, M.-M., Jiang, M.-X., Yang, M.-Y., Wu, Q., Shao, B., & Li, X.-M. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.06.037 · Cited by 50
Metadata
- Authors
- Adams, H. P., Bendixen, B. H., Kappelle, L. J., Biller, J., Love, B. B., Gordon, D. L., & Marsh, E. E.
- Venue
- Stroke
- Published
- 1993-01
- Volume
- 24
- Issue
- 1
- Pages
- 35-41
- ISSN
- 0039-2499 (ISSN-L)
- 1524-4628
- OpenAlex
- W2138595885
- Cited by
- 12,335
- References
- 0
- FWCI
- 15.59
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.