Aging Powerfully: AI Blueprint for Chronic Disease Management and Social Determinants of Longevity: 2025 National Inter-University Health Data and AI Inquiry Program | Indicium 2025 Conference Proceedings
Bibliographic record
Abstract
The National Inter-University Health Data and AI Inquiry Program is a unique, interdisciplinary educational initiative that bridges the gap between traditional academic coursework and real-world data science research. Designed to foster agile learning and critical thinking, the program provides both undergraduate and graduate students with an exceptional opportunity to engage with Open Data and apply advanced data science techniques to meaningful, socially relevant health research challenges. Through this program, students develop and refine skills in uncovering hidden patterns and trends in both structured and unstructured datasets. They gain hands-on experience using a wide range of analytical tools and programming languages—such as Python, R, and diverse machine learning frameworks—to conduct in-depth data analysis. Indicium is an annual research mentorship program and competition designed to introduce undergraduate students to the process of independent scientific inquiry. Tailored for students with limited research experience, Indicium connects participants with mentors from diverse STEM backgrounds—including faculty members, graduate students, and medical professionals—who guide them through every stage of the research process. Throughout the program, students receive mentorship on research design, data analysis, and scientific communication, while also benefiting from skill-building workshops and networking opportunities with the broader scientific community. The theme for 2025, “Aging Powerfully: AI Blueprint for Chronic Disease Management and Social Determinants of Longevity,” challenged participants to explore how data and AI can advance our understanding of longevity and chronic disease management. Their investigations covered a wide array of topics—from designing AI-powered wearables for arthritis care, to leveraging sensor data for monitoring physical activity, to modelling gut-brain axis biomarkers in Alzheimer’s disease using machine learning. We are continually inspired by the intellectual curiosity, creativity, and analytical skills demonstrated by this next generation of researchers. Their work not only contributes to the scientific discourse around longevity and chronic disease management but also exemplifies the spirit of innovation that these programs seek to foster. To showcase and celebrate their achievements, two national conferences were held in July 2025: the Eastern Canada Conference at the University of Toronto (July 10, 2025) and the Western Canada Conference at the University of Calgary (July 18, 2025). On behalf of STEM Fellowship, we extend our heartfelt congratulations to all participants. We also thank our dedicated team of STEM Fellowship volunteers whose tireless support made these programs possible. We would also like to gratefully acknowledge the invaluable partnership and contributions of Research Canada, Canadian Science Publishing, CPHIN, Underline, and Overleaf. Finally, we are deeply grateful to the University of Calgary Institutes for Transdisciplinary Scholarship for their generous support in enabling the publication of these conference proceedings. In partnership with Canadian Personalized Healthcare Innovation Network, Research Canada, Roche, Canadian Science Publishing, Underline.io, Overleaf. This publication was made possible by the generous support of the University of Calgary Institutes for Transdisciplinary Scholarship.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".