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Record W4319332861 · doi:10.5853/jos.2022.02880

Replacing Alteplase with Tenecteplase: Is the Time Ripe?

2023· review· en· W4319332861 on OpenAlexaff
Nishita Singh, Bijoy K. Menon, Adam A. Dmytriw, Robert W. Regenhardt, Joshua A Hirsch, Aravind Ganesh

Bibliographic record

VenueJournal of Stroke · 2023
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of ManitobaUniversity of CalgaryHealth Sciences CentreManitoba Health
Fundersnot available
KeywordsTenecteplaseThrombolysisMedicineClinical trialStroke (engine)Intensive care medicineFibrinolytic agentAcute strokeTissue plasminogen activatorInternal medicineMyocardial infarctionEngineering

Abstract

fetched live from OpenAlex

Thrombolysis for acute ischemic stroke has predominantly been with alteplase for over a quarter of a century. In recent years, with trials showing evidence of higher rates of successful reperfusion, similar safety profile and efficacy of tenecteplase (TNK) as compared to alteplase, TNK has now emerged as another potential choice for thrombolysis in acute ischemic stroke. In this review, we will focus on these recent advances, aiming: (1) to provide a brief overview of thrombolysis in stroke; (2) to provide comparisons between alteplase and TNK for clinical, imaging, and safety outcomes; (3) to focus on key subgroups of interest to understand if there is an advantage of using TNK over alteplase or vice-versa, to review available evidence on role of TNK in intra-arterial thrombolysis, as bridging therapy and in mobile stroke units; and (4) to summarize what to expect in the near future from recently completed trials and propose areas for future research on this evolving topic. We present compelling data from several trials regarding the safety and efficacy of TNK in acute ischemic stroke along with completed yet unpublished trials that will help provide insight into these unanswered questions.

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)0.001
metaresearch head score (Gemma)0.000
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.277
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.036
GPT teacher head0.317
Teacher spread0.281 · 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

Citations21
Published2023
Admission routes1
Has abstractyes

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