Evidence map›Paper›PMID 26322334›Full record

ArticleJournal of neurology & translational neuroscience

Stroke and Cancer- A Complicated Relationship.

Jennifer L Dearborn, Victor C Urrutia, Steven R Zeiler

Open access · greenAbstract read
In one paragraph

Article in Journal of neurology & translational neuroscience. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed, 2 pooled it
3.9field-weighted citation impact, top 6% of its field
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

71 citing papers in PubMed, 2 syntheses or guidelines pooled it, 133 citations in OpenAlex.

  1. Pooled it
  2. Guideline
  3. Article
  4. Detection rate of FDG-PET/CT for occult cancer in patients with cryptogenic ischemic stroke.European journal of nuclear medicine and molecular imaging · 2026
    Observational
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Clinical features and prognostic factors of patients with cancer-associated stroke.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024
    Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

11 more citing papers are in PubMed but not listed here.

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

3 authors.

Jennifer L Dearborn
Victor C Urrutia
Steven R Zeiler

Funding

The contributions of motor learning and inhibitory balance to stroke recoveryK08NS085033 · NINDS · JOHNS HOPKINS UNIVERSITY · PI ZEILER, STEVEN R · 2013 to 2017
$951k
NINDS NIH HHS K08 NS085033
6 · The paper itself

Abstract

The interrelationship between stroke and cancer is complex. Cancer and stroke may occur independently in a given patient, or cancer may directly or indirectly lead to stroke via: hypercoaguability, non-bacterial thrombotic endocarditis (NBTE), direct tumor compression of blood vessels, or treatment-related effects which potentiate stroke. Patients with cryptogenic stroke are relatively common, and under the right circumstances, may provide an opportunity to screen for occult malignancy. In this review, we discuss relevant data linking stroke and cancer as well as propose a testable algorithm for cancer screening in the patient with cryptogenic stroke. Future directions should focus on validating patient-care algorithms in prospective clinical trials to provide an evidence base for this important issue.

Indexed as

CancerHypercoagulable StatesNon- Bacterial Thrombotic EndocarditisStrokeVenous Thromboemoblism

Identifiers

PMID26322334
PMCPMC4550304
OpenAlexW2184679832

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.