← Back to cohort
Pragmatic solutions to reduce the global burden of stroke: a World Stroke Organization–Lancet Neurology Commission
Valery L. Feigin, Mayowa Owolabi, Foad Abd-Allah, Rufus Akinyemi, Natalia V. Bhattacharjee, Michael Brainin, Jackie Cao, Valeria Caso, Bronte Dalton, Alan Davis, Robert J. Dempsey, Joseph Duprey, Wuwei Feng, Gary A. Ford, Seana Gall, Dorcas B.C. Gandhi, David C. Good, Vladimir Hachinski, Werner Hacke, Graeme J. Hankey, Marie Ishida, Walter D. Johnson, Julie Kim, Pablo M. Lavados, Patrice Lindsay, Ajay Mahal, Sheila Martins, Christopher J L Murray, Thuy Phuong Nguyen, Bo Norrving, Muideen T. Olaiya, Oladotun Olalusi, Jeyaraj Pandian, Hoang Phan, Thomas Platz, Annemarei Ranta, Sabah Rehman, Greg Roth, Ivy Sebastian, Amanda Smith, Nijasri C. Suwanwela, PN Sylaja, Rajshree Thapa, Amanda G. Thrift, Ezinne Uvere, Dileep R. Yavagal, Joseph Yaria, Semaw Ferede Abera, Norlinah Mohamed Ibrahim, Liping Liu, Bruce Ovbiagele, М. А. Пирадов, Carlos Abanto, Adamu Addissie, Amos O. Adeleye, Yerzhan Adilbekov, B. Adilbekova, Thierry Adoukonou, Diana Aguiar de Sousa, З Б Ахметжанова, Albert Akpalu, Mustapha El Alaoui-Faris, Sebastián F. Ameriso, Silva Andonova, Anita Arsovska, Folorunso E Awoniyi, Moiz Bakhiet, Miguel A. Barboza, Hamidon Basri, Philip M. Bath, Dániel Bereczki, Simone Beretta, Aaron L. Berkowitz, Julie Bernhardt, Guna Bērziņa, Bhavan Bhavsar, Mher S Bisharyan, Manoj Bohara, Pascal Bovet, Hrvoje Budinčević, Dominique A. Cadilhac, Denis Čerimagić, Augustina Charway‐Felli, Christopher Chen, Jerome H. Chin, Hanne Christensen, Kamil Chwojnicki, Adriana Bastos Conforto, Manuel Correia, Daissy Liliana Mora Cuervo, Anna Członkowska, Marco D’Amelio, Kristine Danielyan, Stephen M. Davis, Vida Demarin, Andrew M. Demchuk, Martin Dichgans, Клара Докова, Geoffrey A. Donnan, Juan Carlos Durán, Gloria Ekeng, Mitchell S Elkind, Matthias Endres, Urs Fischer, Yuriy Flomin, Fortuné Gbètoho Gankpé, Martín Gavidia, Mehari Gebreyohanns, Mary G. George, Marek Gierlotka, Maurice Giroud, E. V. Gnedovskaya, Ivete Pillo Gonçalves, Fernando Góngora‐Rivera, Padma Gunaratne, Randah R Hamadeh, Talhatu K. Hamzat, Mirjam R. Heldner, Etedal Ahmed A. Ibrahim, Hanne Ihle-Hansen, Sungju Jee, Jeng Jiann-Shing, S Clay Johnston, D Jovanović, Kristaps Jurjāns, Yogeshwar Kalkonde, Saltanat Kamenova, Bartosz Karaszewski, Peter J. Kelly, Stefan Kiechl, Аida Kondybayeva, Janika Kõrv, Grzegorz Kozera, Michael Kravchenko, Yakup Krespi, Rita Krishnamurthi, Jera Kruja, Kürşad Kutluk, Peter Langhorne, Zhe Kang Law, Dmytro Lebedynets, Tsong‐Hai Lee, Thomas Leung, David S Liebeskind, Patricio López‐Jaramillo, Paulo A. Lotufo, M. Julia Machline-Carrion, Luı́s F. Maia, Branko Malojčić, Hugh S. Markus, Juan Manuel Márquez-Romero, Marco T. Medina, Sabina Medukhanova, Man Mohan Mehndiratta, Evija Miglāne, Illa Mihejeva, Robert Mikulík, Erkin М Мirrakhimov, Sunil Munakomi, Séan Murphy, Kamarul Imran Musa, Ahmed Nasreldein, Raul G. Nogueira, Christian H. Nolte, Jean Jacques Noubiap, Nelson Novarro, Cassandra Ocampo, Martin O’Donnell, Yomi Ogun, Adesola Ogunniyi, Mohammed I. Oraby, Dilek Neci̇oğlu Örken, Atilla Özcan Özdemi̇r, Şerefnur Öztürk, Mélanie Paccot, Telmo Pereira, André Peeters, Tatjana Potpara, Hariklia Proios, Farooq Azam Rathore, Ralph L. Sacco, Ramesh Sahathevan, Else S Sandset, Irving Renato Santos, Gustavo Saposnik, Fred Stephen Sarfo, João Sargento‐Freitas, Mukul Sharma, Louise Shaw, Kevin N. Sheth, Yong‐Il Shin, A Shobhana, S. Silva, Vítor Tedim Cruz, Kiran T. Thakur, Lekh Jung Thapa, Danilo Toni, M.A. Topcuoglu, Júlio Torales, Amytis Towfighi, Thomas Truelsen, Alexander Tsiskaridze, Marshall K. Tulloch‐Reid, Juan Nicolás Useche, Peter Vanacker, Sophia Vassilopoulou, Gorana Vukorepa, Vladimira Vuletić, Kolawole Wahab, Wenzhi Wang, Tissa Wijeratne, Bogdan Wojtyniak, Charles Wolfe, Mapoure Njankouo Yacouba, Jie Yang, Yared Mamushet Yifru, Adriana Yock‐Corrales, Naohiro Yonemoto, Laetitia Yperzeele, Paweł Zagożdżon
Bibliographic record
VenueThe Lancet Neurology · 2023
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteHacettepe ÜniversitesiHealth Research Council of New ZealandTartu ÜlikoolAfrican Academy of SciencesMedical Research CouncilUniwersytet OpolskiBrain Research New ZealandUniversität InnsbruckNational Taiwan University HospitalRoyal Hobart Hospital Research FoundationNational Institute on AgingUniversity College DublinUniversité de BourgogneChang Gung Medical FoundationSchool of Medicine, Johns Hopkins UniversityNational and Kapodistrian University of AthensUniversity of WashingtonBill and Melinda Gates FoundationNational Heart Foundation of AustraliaChang Gung UniversityPusan National UniversityAl-Farabi Kazakh National UniversityChungnam National University HospitalFogarty International CenterNational Institutes of HealthChungnam National UniversityYale UniversityUniversity of GlasgowUniversity of Southern CaliforniaKing's College LondonMedizinische Universität InnsbruckNational Institute of Neurological Disorders and StrokeUniversiti Kebangsaan MalaysiaInselspital, Universitätsspital BernNational Health and Medical Research CouncilArabian Gulf UniversityUniversidad El BosqueAuckland University of Technology, New ZealandAlzheimer's AssociationGlobal Brain Health InstituteStroke FoundationIrving Medical Center, Columbia UniversityUniversity of BernHamilton Health SciencesNational Taiwan UniversityRoyal SocietyNational Human Genome Research InstituteRigshospitaletJohns Hopkins University
KeywordsStroke (engine)MedicineHealth carePsychological interventionEnvironmental healthEconomic growthNursing
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)0.012
metaresearch head score (Gemma)0.020
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 score0.012
Threshold uncertainty score0.065
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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 head0.073
GPT teacher head0.353
Teacher spread0.280 · 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 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
Citations668
Published2023
Admission routes1
Has abstractno