Acute Stroke Research: Being Part of a Game-Changer with Dr. Dar Dowlatshahi, Scientific Director of the Ottawa Stroke Program
Bibliographic record
Abstract
ABSTRACT:Dr. Dar Dowlatshahi, MD/PhD, is a stroke neurologist, an assistant professor at the University of Ottawa, and a neuroscientist at the Ottawa Hospital Research Institute (OHRI). As the Scientific Director of the Ottawa Stroke Program, he is conducting cutting-edge research in the area of acute stroke, with a special interest in intracerebral hemorrhage (ICH). He was part of the recent ESCAPE trial, a national groundbreaking study that has redefined the scope of stroke therapy around the world. We had the incredible opportunity of speaking with Dr. Dowlatshahi about his exciting career as a clinician-scientist, as he educated us about the unique features of stroke, informed us of the recent advancements in his research, and provided advice for interested students and trainees who want to pursue a career in academic medicine.RÉSUMÉ: Dr. Dar Dowlatshahi, MD/PhD, est un neurologue spécialisé en AVC, professeur adjoint à l’Université d’Ottawa, et un neuroscientifique à l’Institut de recherche en santé d’Ottawa (IRSO). Comme directeur scientifique du Programme d’AVC à Ottawa, il mène des recherches de pointe dans le domaine de l’AVC aigu, avec un intérêt particulier dans l’hémorragie intracérébrale (HIC). Il a fait partie de l’essai récent « ESCAPE », une étude révolutionnaire nationale qui a redéfini le cadre de la thérapie de l’AVC autour du monde. Nous avons eu l’incroyable opportunité de parler avec le Dr. Dowlatshahi à propos de sa carrière passionnante comme clinicien-chercheur. Il nous informa ainsi sur les caractéristiques uniques de l’AVC, des récents progrès dans ses recherches, et nous a fourni des conseils pour les étudiants et stagiaires voulant poursuivre une carrière en médecine académique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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.
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 teacher head, 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".