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Thrombectomy alone versus intravenous alteplase plus thrombectomy in patients with stroke: an open-label, blinded-outcome, randomised non-inferiority trial
Urs Fischer, Johannes Kaesmacher, Daniel Strbian, Omer Eker, Christophe Cognard, Patricia S Plattner, Lukas Bütikofer, Pasquale Mordasini, Sandro Deppeler, Vítor Mendes Pereira, Jean François Albucher, Jean Darcourt, Romain Bourcier, B. Guillon, Chrysanthi Papagiannaki, Ozlem Ozkul-Wermester, Gerli Sibolt, Marjaana Tiainen, Benjamin Gory, Sébastien Richard, Jan Liman, Marielle Ernst, Marion Boulanger, Charlotte Barbier, Laura Mechtouff, Liqun Zhang, Gaultier Marnat, Igor Sibon, Omid Nikoubashman, Arno Reich, Arturo Consoli, Bertrand Lapergue, Marc Ribó, Alejandro Tomasello, Suzana Saleme, Francisco Macian, Solène Moulin, Paolo Pagano, Guillaume Saliou, Emmanuel Carrera, Kévin Janot, María Hernández‐Pérez, Raoul Pop, Lucie Della Schiava, Andreas R. Luft, Michel Piotin, Jean‐Christophe Gentric, Aleksandra Pikula, Waltraud Pfeilschifter, Marcel Arnold, Adnan Siddiqui, Michael T. Froehler, Anthony J. Furlan, René Chapot, Martin Wiesmann, Paolo Machi, Hans‐Christoph Diener, Zsolt Kulcsár, Leo H. Bonati, Claudio L. Bassetti, Mikaël Mazighi, David S. Liebeskind, Jeffrey L. Saver, Jan Gralla, Angelika Alonso, Caroline Arquizan, Xavier Barreau, Rémy Beaujeux, Daniel Behme, Tobias Boeckh‐Behrens, Christian Boehme, Martí Boix, Grégoire Boulouis, Nicolas Bricout, Nicolas Broc, Carlo W. Cereda, Emmanuel Chabert, Tae‐Hee Cho, Alessandro Cianfoni, Vincent Costalat, Christian Denier, Frederico Di Maria, Richard du Mesnil de Rochemont, Patricia Fearon, Anna Ferrier, Sebastian Fischer, Maxime Gauberti, Marie Gaudron, Laëtitia Gimenez, Christoph Globas, Michael Görtler, Mayank Goyal, Ruediger Hilker-Roggendorf, Michael D. Hill, Vi Tuan Hua, Lisa Humbertjean, Olav Jansen, Simon Jung, Georg Kägi, Michael Kelly, Ilka Kleffner, Michael Knoflach, Krassen Nedeltchev, Lars Udo Krause, Kimmo Lappalainen, Margaux Lefebvre, Joe Leyon, Liang Liao, Jean-Sébastien Liegey, Christian Loehr, Patrik Michel, Stefania Nannoni, Patrick Nicholson, Lorena Nico, Michaël Obadia, Julien Ognard, Ayokunle Ogungbemi, Jean‐Marc Olivot, Simon Escalard, Marco Pasi, Lissa Peeling, Jane Perez, Martina Petersen, Eike I. Piechowiak, Roberto Raposo, Silja Räty, Sarah C. Reitz, Sebastián Remollo, Luca Remonda, Ian Rennie, Manuel Requena, Alexander Riabikin, Roberto Riva, Aymeric Rouchaud, Andrea Rosi, Marta Rubiera, Laurent Spelle, Marlena Schnieder, Joanna D. Schaafsma, Tilman Schubert, Jörg B. Schulz, Mohammed Siddiqui, Sébastien Soize, Michael Sonnberger, Emmanuel Touzé, Aude Triquenot, Guillaume Turc, Lucy Vieira, Wagih Ben Hassen, Judith Wagner, Katrin Wasser, Johannes Weber, Holger Wenz, David Weisenburger‐Lile, Fritz Wodarg, Valérie Wolff, Silke Wunderlich
Bibliographic record
VenueThe Lancet · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsToronto Western HospitalUniversity of TorontoSt. Michael's Hospital
FundersGenentechDeutsche ForschungsgemeinschaftInselspital, Universitätsspital BernSchweizerische HerzstiftungSapheonNational Institutes of HealthSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMedtronicBayerAlexion PharmaceuticalsUniversität ZürichStrykerPfizerNational Science Foundation
KeywordsMedicineModified Rankin ScaleClinical endpointStroke (engine)SurgeryRandomized controlled trialOcclusionStentIntention-to-treat analysisConfidence intervalSolitaire Cryptographic AlgorithmRandomizationBolus (digestion)AnesthesiaInternal medicineIschemic strokeIschemia
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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 head0.064
GPT teacher head0.335
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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 designRandomized trial
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
Citations285
Published2022
Admission routes1
Has abstractno