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Record W2883313499 · doi:10.1177/1747493018790024

Tenecteplase for the treatment of acute ischemic stroke: A review of completed and ongoing randomized controlled trials

2018· review· en· W2883313499 on OpenAlexafffund
Shelagh B. Coutts, Eivind Berge, Bruce Campbell, Keith W. Muir, Mark Parsons

Bibliographic record

VenueInternational Journal of Stroke · 2018
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchBritish Heart Foundation
KeywordsTenecteplaseMedicineThrombolysisTissue plasminogen activatorFibrinolytic agentStroke (engine)Thrombolytic drugClinical trialReteplaseIntensive care medicineMyocardial infarctionCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Alteplase has been the mainstay of thrombolytic treatment since the National Institutes of Neurological Disorders and Stroke trial was published in 1995. Over recent years, several trials have investigated alternative thrombolytic agents. Tenecteplase, a genetically engineered mutant tissue plasminogen activator, has a longer half-life, allowing single intravenous bolus administration without infusion, is more fibrin specific, produces less systemic depletion of circulating fibrinogen, and is more resistant to plasminogen activator inhibitor compared to alteplase. Tenecteplase is established as the first-line intravenous thrombolytic drug for myocardial infarction, where it has been shown to achieve comparable reperfusion with reduced risk of systemic bleeding in comparison to alteplase. We review the literature on tenecteplase for the treatment of acute ischemic stroke, with a focus on the major completed and ongoing trials. Overall, tenecteplase shows promise for treatment of acute ischemic stroke, both in populations currently eligible for alteplase and also in groups not currently treated with thrombolysis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.638
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0130.006
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.066
GPT teacher head0.397
Teacher spread0.331 · 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 teacher head, 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

Citations53
Published2018
Admission routes2
Has abstractyes

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