MJM MedTalks (S02E01): DIY Automated Insulin Delivery
Bibliographic record
Abstract
McGill Journal of Medicine (MJM) MedTalks is a Podcast series where members of the medical and health science communities from McGill are interviewed on topics related to career, research, advocacy and more. In season 2, we are opening up the conversation to members of the academic community beyond McGill University. The aim of MedTalks is to open a space where experienced professionals and researchers can share information and advice for trainees in healthcare and medical sciences. In this episode, McGill doctoral candidate and MJM Editor and Podcast member, Meryem Talbo, interviews four guest-experts on the topic of do-it-yourself (DIY) automated insulin delivery (AID) systems for people living with type 1 diabetes, with the aim of demystifying this novel and promising technology. The panel includes Kate Farnsworth, a patient-advocate and founder of an online DIY AID community of over 30,000 people; Dr. Ilana Halpern, an endocrinologist at Sunnybrook Health Sciences Centre in Toronto, with over 10 years of experience in the field; Dr. Maha Lebbar, an endocrinologist/diabetologist and current M.Sc. candidate at the University of Montreal with special interests in new technologies for type 1 diabetes; and Dr. Zekai Wu, a physician and current postdoctoral fellow at the Institut de recherches cliniques de Montréal (IRCM) and McGill University, who introduced the DIY AID system into China and is currently working on new type 1 diabetes technologies. This conversation covers the development of DIY AID technologies, their availability and accessibility to people living with type 1 diabetes, as well as the legality and regulatory frameworks that underlie their use. The show notes include a glossary of terms, links to publications, images, and videos referenced in the episode, and a transcript of Meryem Talbo’s conversation with the guest panel.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.264 | 0.057 |
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".