Foundations and Strategic Vision of the Canadian Translational Geroscience Network
Bibliographic record
Abstract
Geroscience is an emerging interdisciplinary field that explores the biological connections between aging and the development of chronic diseases, with the ultimate goal of identifying interventions to extend healthspan and delay age-related conditions. Recognizing the growing importance of this field, the Canadian Translational Geroscience Network (geroscience.ca) was officially launched during a conference held in Montreal on September 5-6, 2024. Building on the momentum of successful Geroscience meetings in Toronto and Montreal in 2023, this milestone event marked a transformative step forward for geroscience in Canada. This event brought together key stakeholders, including the Canadian Frailty Network (CFN), the Canadian Institutes of Health Research Institute of Aging (CIHR-IA), the Réseau Québécois de Recherche sur le Vieillissement (RQRV), the Simone & Edouard Schouela RUISSS McGill Centre of Excellence for Sustainable Health of Seniors (Schouela CEDurable), the Division of Geriatric Medicine at McGill University, and the Department of Biochemistry at the University of Toronto. Additionally, a broad coalition of geriatricians, healthcare professionals, and researchers convened to discuss and advance the field of geroscience in Canada. The 2-day conference focused on creating a multidisciplinary community to address the challenges of an aging population, emphasizing the importance of funding, national and international collaboration, and training the next generation of researchers and clinicians. Workshops and presentations showcased a range of innovative research, from cellular studies to clinical trials, aimed at understanding and treating age-related diseases. Key discussions highlighted the critical role of partnerships among research institutions, healthcare systems, and biotech companies in translating research findings into practical interventions. The Canadian Translational Geroscience Network's strategic objectives focus on expanding funding opportunities for geroscience, developing specialized training programs, and increasing membership to cultivate a diverse, multidisciplinary, and collaborative network. This network aims to include students, basic and clinical researchers, citizens, government entities, and organizations or professionals interested in advancing the geroscience field. With a clear roadmap for future growth, the Canadian Translational Geroscience Network aims to position Canada at the forefront of geroscience, fostering evidence-based innovation that improves the health and quality of life for aging populations.
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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 | 0.045 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".