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Record W4318924378 · doi:10.1161/strokeaha.122.041061

Tenecteplase Treatment and Thrombus Characteristics Associated With Early Reperfusion: An EXTEND-IA TNK Trials Analysis

2023· article· en· W4318924378 on OpenAlexfundno aff
Vignan Yogendrakumar, Leonid Churilov, Prodipta Guha, James Beharry, Peter Mitchell, Timothy Kleinig, Nawaf Yassi, Vincent Thijs, Teddy Y. Wu, Helen Brown, Helen M. Dewey, Tissa Wijeratne, Bernard Yan, Gagan Sharma, Patricia Desmond, Mark Parsons, Geoffrey A. Donnan, Stephen M. Davis, Bruce Campbell, Richard Dowling, Steven Bush, Rebecca Scroop, Mark Brooks, Hamed Asadi, Timothy Ang, Ferdinand Miteff, Christopher Levi, Henry Zhao, Felix Ng, Fana Alemseged, Henry E. Rice, Laetitia de Villiers, Kendal Redmond, David Leggett

Bibliographic record

VenueStroke · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersMedical Research CouncilCanadian Institutes of Health ResearchUniversity of MelbourneNational Health and Medical Research CouncilRoyal College of Physicians and Surgeons of Canada
KeywordsTenecteplaseMedicineThrombusFibrinolytic agentOdds ratioStroke (engine)CardiologyInternal medicineOcclusionThrombolysisTissue plasminogen activatorMyocardial infarction

Abstract

fetched live from OpenAlex

Background: Intracranial occlusion site, contrast permeability, and clot burden are thrombus characteristics that influence alteplase-associated reperfusion. In this study, we assessed the reperfusion efficacy of tenecteplase and alteplase in subgroups based on these characteristics in a pooled analysis of the EXTEND-IA TNK trial (Tenecteplase Versus Alteplase Before Endovascular Therapy for Ischemic Stroke). Methods: Patients with large vessel occlusion were randomized to treatment with tenecteplase (0.25 or 0.4 mg/kg) or alteplase before thrombectomy in hospitals across Australia and New Zealand (2015–2019). The primary outcome, early reperfusion, was defined as the absence of retrievable thrombus or >50% reperfusion on first-pass angiogram. We compared the effect of tenecteplase versus alteplase overall, and in subgroups, based on the following measured with computed tomography angiography: intracranial occlusion site, contrast permeability (measured via residual flow grades), and clot burden (measured via clot burden scores). We adjusted for covariates using mixed effects logistic regression models. Results: Tenecteplase was associated with higher odds of early reperfusion (75/369 [20%] versus alteplase: 9/96 [9%], adjusted odds ratio [aOR], 2.18 [95% CI, 1.03–4.63]). The difference between thrombolytics was notable in occlusions with low clot burden (tenecteplase: 66/261 [25%] versus alteplase: 5/67 [7%], aOR, 3.93 [95% CI, 1.50–10.33]) when compared to high clot burden lesions (tenecteplase: 9/108 [8%] versus alteplase: 4/29 [14%], aOR, 0.58 [95% CI, 0.16–2.06]; P interaction =0.01). We did not observe an association between contrast permeability and tenecteplase treatment effect (permeability present: aOR, 2.83 [95% CI, 1.00–8.05] versus absent: aOR, 1.98 [95% CI, 0.65–6.03]; P interaction =0.62). Tenecteplase treatment effect was superior with distal M1 or M2 occlusions (53/176 [30%] versus alteplase: 4/42 [10%], aOR, 3.73 [95% CI, 1.25–11.11]), but both thrombolytics had limited efficacy with internal carotid artery occlusions (tenecteplase 1/73 [1%] versus alteplase 1/19 [5%], aOR, 0.22 [95% CI, 0.01–3.83]; P interaction =0.16). Conclusions: Tenecteplase demonstrates superior early reperfusion versus alteplase in lesions with low clot burden. Reperfusion efficacy remains limited in internal carotid artery occlusions and lesions with high clot burden. Further innovation in thrombolytic therapies are required.

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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.009
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.322
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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

Citations32
Published2023
Admission routes1
Has abstractyes

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