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Record W4396977901 · doi:10.4081/btvb.2024.117

Cancer-associated ischemic stroke: current knowledge and future directions

2024· article· en· W4396977901 on OpenAlexaff
Ronda Lun, Deborah Siegal

Bibliographic record

VenueBleeding Thrombosis and Vascular Biology · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsCurrent (fluid)Stroke (engine)Ischemic strokeMedicinePhysical medicine and rehabilitationCardiologyEngineeringIschemiaElectrical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

Because cancer is a strong prothrombotic, there is an increased risk of thromboembolism, which includes ischemic stroke, especially in the first six to twelve months following a cancer diagnosis. The risk of ischemic stroke differs according to the location and stage of cancer. Given that the risk increases prior to a cancer diagnosis, stroke may be the initial sign of occult cancer. Although data on the risk, treatment, and outcomes of cancer-associated stroke are more limited than those on cancer-associated venous thromboembolism, the condition is still recognized as a thrombotic complication of cancer. Up to 10% of ischemic stroke patients also have a concurrent cancer diagnosis, and these patients seem to have higher short-term mortality and morbidity rates. With more people expected to survive longer after cancer treatment and an increasing number of cancer survivors, the burden of stroke among cancer patients is predicted to rise. This narrative review aims to provide an overview of the pathophysiologic mechanisms, treatment options, and epidemiology of ischemic stroke, including cancer screening for those who have cryptogenic (unexplained) stroke.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.330
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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