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Cited by 1,116

The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index.

Stroke

Campbell, Bruce C V, Khatri, Pooja·2020·The Lancet

Abstract

Stroke is a major cause of death and disability globally. Diagnosis depends on clinical features and brain imaging to differentiate between ischaemic stroke and intracerebral haemorrhage. Non-contrast CT can exclude haemorrhage, but the addition of CT perfusion imaging and angiography allows a positive diagnosis of ischaemic stroke versus mimics and can identify a large vessel occlusion target for endovascular thrombectomy. Management of ischaemic stroke has greatly advanced, with rapid reperfusion by use of intravenous thrombolysis and endovascular thrombectomy shown to reduce disability. These therapies can now be applied in selected patients who present late to medical care if there is imaging evidence of salvageable brain tissue. Both haemostatic agents and surgical interventions are investigational for intracerebral haemorrhage. Prevention of recurrent stroke requires an understanding of the mechanism of stroke to target interventions, such as carotid endarterectomy, anticoagulation for atrial fibrillation, and patent foramen ovale closure. However, interventions such as lowering blood pressure, smoking cessation, and lifestyle optimisation are common to all stroke subtypes.

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Metadata

Authors
Campbell, B. C. V., & Khatri, P.
Venue
The Lancet
Publisher
Elsevier BV
Published
2020-07
Volume
396
Issue
10244
Pages
129-142
ISSN
  • 0099-5355 (ISSN-L)
  • 0140-6736
OpenAlex
W4210440929
Cited by
1,116
References
122
FWCI
77.26
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Stroke. https://app.cochat.ai/papers/10.1016/s0140-6736(20)31179-x
The paper
Campbell, B. C. V., & Khatri, P. (2020). Stroke. The Lancet. https://doi.org/10.1016/s0140-6736(20)31179-x

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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.