Cutting-edge Innovations in Cardiac Health: Galvanic Insights from a Clinician-Scientist, Dr. Benjamin Hibbert
Bibliographic record
Abstract
Dr. Benjamin Hibbert, MD, PhD, FRCPC is an interventional cardiologist, an assistant professor, as well as the director of the Vascular Biology and Experimental Medicine Laboratory at the University of Ottawa Heart Institute (UOHI). With a focus on performing revolutionary bench-to-bedside research, Dr. Hibbert’s clinical and basic science research interests include the development of novel cardiac biomarkers, elucidating the mechanisms that underlie pathological arterial remodelling in transplant vasculopathy, and the pharmacodynamics of adjuvant antiplatelet and antithrombotic agents in cardiac disease. We had the privilege of speaking with Dr. Hibbert about his career path, research experiences, and perspectives on the importance of the clinician-investigator program in training the oncoming generation of clinician-scientists.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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.
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".