Bibliographic record
Abstract
On 2 April 2025, the cardiovascular research community lost an exceptional scientist and leader with the sudden passing of Dr Gary D. Lopaschuk. For nearly 40 years, Gary was at the forefront of advancing knowledge in cardiac energy metabolism, dedicating his career to a field in which he made many transformative contributions. Gary’s research journey started at the University of British Columbia (UBC), where he first earned a Bachelor of Science in Pharmacy. He then completed an MSc degree in Pharmacology and Toxicology under the mentorship of Dr S. Katz, before continuing on at UBC for doctoral studies with Drs J.H. McNeill and S. Katz. Following his PhD, he undertook postdoctoral research at the Milton S. Hershey Medical Center in Pennsylvania, where he worked with Dr J.R. Neely. This experience was formative, cementing what would become his lifelong focus on the study of cardiac metabolism and laying the groundwork for his distinguished scientific career. In 1985, Gary began his independent career at The Hospital for Sick Children in Toronto, contributing to the newly created Cardiovascular Group within the Departments of Pediatrics and Pharmacology. Following this, Gary joined the Department of Pediatrics at the University of Alberta, where his career flourished for nearly four decades. His impact on the institution and the field was widely recognized, and he was appointed a Distinguished University Professor, one of the university’s highest academic honours. In 2020, he received the University Cup, the most prestigious award bestowed by the University of Alberta, recognizing excellence in research, mentorship, and service. Until his passing, Gary remained fully engaged in research, with multiple trainees, numerous invited presentations, and several grants. Over the course of his career, Gary became one of the world’s leading authorities on cardiac energy metabolism. His pioneering studies reshaped the understanding of how the heart produces and utilizes energy, particularly under pathological conditions such as ischaemia, heart failure, and diabetes. His service to the wider scientific community was also significant, involving mentorship of the next generation of scientists, committee memberships, grant review panels, and contributing to the peer review process for numerus journals. For over two decades, Gary was a dedicated reviewer for Cardiovascular Research. His contributions to the journal were significant as he routinely provided between 10 and 15 manuscript evaluations each year. His reviews, frequently recognized among the most valued by editors, were distinguished by their rigour, fairness, and thoughtful critique. This commitment reflected Gary’s belief that advancing science required not only the generation of new data but also careful stewardship of the published record. Through this service, he shaped the field in ways less visible than his own publications, but no less impactful. The loss of Dr Lopaschuk is deeply felt across the University of Alberta, throughout Canada, and around the world. His research redefined a field, his mentorship launched careers, and his service strengthened the broader scientific community. The legacy he leaves behind is one of scientific excellence, intellectual leadership, and dedication to the advancement of knowledge.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.038 |
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".