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 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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.037 | 0.022 |
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".