Drug Therapy for Stroke Prevention
Bibliographic record
Abstract
1. Stroke Prevention in 2000 J. Easton 2. Primary vs. Secondary Stroke Prevention P. Gorelick 3. Aspirin P. De Moerloose 4. Ticlopidine and Clopidogrel M. Paciaroni 5. Other Anti-platelet Agents A. Culebras 6. Dipyridamole H. Diener 7. New Antiplatelet Agents B. Weksler 8. Clinical Use of Antiplatelet Agents for Stroke Prevention G. Donnan 9. Antithrombotic Therapy Before and After Carotid Endarterectomy H. Barnett 10. Anticoagulants T. Moulin 11. Early Prevention of Stroke Recurrence M. Brown 12. Lipid Lowering Agents and Stroke Risk P. Amarenco 13. Stroke Prevention With Blood Pressure Control P. Bath 14. Antioxidants, Vitamins D. Spence 15. Estrogen Replacement L. Brass 16. Healthcare Issues, Benefits and Costs G. Hankey Gorelick, Centre for Stroke Research, USA, Babette Weksler, Cornell University, USA, Geoffrey Donnan, National Stroke Research Institute, Australia, H. Barnett, Robarts Research Institute, Canada, Martin Brown, University of London, UK, Pierre Amarenco, Hospital Lariboisere, France, Phillipe Bath, Nottingham City Hospital, UK, J. David Spence, Robarts Research Institute, Canada, Graeme Hankey, Royal Perth Hospital, Australia, H. Diener, Neurologische Uniklinik, Germany, Lawrence Brass, Yale University School of Medicine, USA, Thierry Moulin, Hospital Regional de Bescancon, Maurizio Paciaroni, University of Perugia, Italy, Antonio Culebras, VA Medical Centre, USA, Phillipe de Moerloose, HUG, Geneva.
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 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.145 | 0.116 |
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.
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.
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".