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Development of imaging-based risk scores for prediction of intracranial haemorrhage and ischaemic stroke in patients taking antithrombotic therapy after ischaemic stroke or transient ischaemic attack: a pooled analysis of individual patient data from cohort studies
Jonathan G. Best, Gareth Ambler, Duncan Wilson, Keon‐Joo Lee, Jae‐Sung Lim, Masayuki Shiozawa, Masatoshi Koga, Linxin Li, Caroline Lovelock, Hugues Chabriat, Michael G. Hennerici, Yuen Kwun Wong, Henry Ma, Luís Prats‐Sánchez, Alejandro Martínez‐Domeño, Shigeru Inamura, Kazuhisa Yoshifuji, Ethem Murat Arsava, Solveig Horstmann, Jan Purrucker, Bonnie Lam, Adrian Wong, Young Dae Kim, Tae‐Jin Song, Robin Lemmens, Sebastian Eppinger, Thomas Gattringer, Ender Uysal, Zeynep Tanrıverdi, Natan M. Bornstein, Einor Ben Assayag, Hen Hallevi, Jeremy Molad, Masashi Nishihara, Jun Tanaka, Shelagh B. Coutts, Alexandros A. Polymeris, Benjamin Wagner, David Seiffge, Philippe Lyrer, Ale Algra, L. Jaap Kappelle, Rustam Al‐Shahi Salman, Hans Rolf Jäger, Gregory Y.H. Lip, Urs Fischer, Marwan El‐Koussy, Jean‐Louis Mas, Laurence Legrand, Christopher Karayiannis, Thanh G. Phan, Sarah Gunkel, Nicolas Christ, Jill Abrigo, Thomas Leung, Chiu‐Wing Winnie Chu, Francesca M. Chappell, Stephen Makin, Derek Hayden, David Williams, Werner H. Mess, Paul J. Nederkoorn, Carmen Barbato, Simone Browning, Anil M. Tuladhar, Noortje A.M. Maaijwee, Anne Cristine Guevarra, Chathuri Yatawara, Anne‐Marie Mendyk, Christine Delmaire, Sebastian Köhler, Robert van Oostenbrugge, Ying Zhou, Chao Xu, Saima Hilal, Bibek Gyanwali, Christopher Chen, Min Lou, Julie Staals, Régis Bordet, Nagaendran Kandiah, Frank‐Erik de Leeuw, Robert Simister, Jeroen Hendrikse, Joanna M. Wardlaw, Yannie Soo, Felix Fluri, Velandai Srikanth, D. Calvet, Simon Jung, Vincent I.H. Kwa, Stefan T. Engelter, Nils Peters, Eric E. Smith, Hideo Hara, Yusuke Yakushiji, Dilek Neci̇oğlu Örken, Franz Fazekas, Vincent Thijs, Ji Hoe Heo, Vincent Mok, Roland Veltkamp, Hakan Ay, Toshio Imaizumi, Beatriz Gómez‐Ansón, Kui Kai Lau, Éric Jouvent, Peter M. Rothwell, Ḱazunori Toyoda, Hee‐Joon Bae, Joan Martí‐Fábregas, David J. Werring
Bibliographic record
VenueThe Lancet Neurology · 2021
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of Calgary
FundersHorizon 2020National Medical Research CouncilMedical Research CouncilCanadian Institutes of Health ResearchBayer YakuhinChest Heart and Stroke ScotlandAstraZeneca KoreaSchweizerische HerzstiftungEisaiUniversity College London Hospitals NHS Foundation TrustMinistry of Health, Labour and WelfareHartstichtingDaiichi-SankyoZonMwSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungFondation LeducqAgence Nationale de la RechercheWellcome TrustUniversity College LondonBritish Heart FoundationHealth Research BoardNational Institute for Health and Care ResearchNIHR Imperial Biomedical Research CentreManchester Biomedical Research CentreYuhanEuropean Regional Development FundBoehringer Ingelheim JapanSanofiNational Cerebral and Cardiovascular CenterNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchInstituto de Salud Carlos IIIAmgenMrs Gladys Row Fogo Charitable TrustAlzheimer's SocietyAlnylam PharmaceuticalsDaiichi Sankyo EuropeServierBayerPfizerStroke AssociationBiogenEuropean CommissionAstraZenecaBristol-Myers SquibbJapan Agency for Medical Research and DevelopmentImperial College LondonNational Science Foundation
KeywordsIschaemic strokeAntithromboticMedicineStroke (engine)CardiologyInternal medicineBrain ischemiaIschemia
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)0.043
metaresearch head score (Gemma)0.061
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 score0.043
Threshold uncertainty score0.228
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.022 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 head0.074
GPT teacher head0.334
Teacher spread0.260 · 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 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
Citations57
Published2021
Admission routes2
Has abstractno