MJM MedTalks (S02E03): Beyond the Hype - GLP-1 Agonists Redefining Glycemic Control
Bibliographic record
Abstract
McGill Journal of Medicine (MJM) MedTalks is a Podcast series where members of the medical and health science communities from McGill and beyond are interviewed on topics related to career, research, advocacy and more. 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, Susan Wang, MJM Podcast Team Co-Lead and first year Internal Medicine Resident at McGill University interviews guest-experts and endocrinologists Dr. Michael Tsoukas, Assistant Professor in the Department of Medicine at McGill University, and Dr. Vanessa Tardio, Assistant Professor in the Department of Medicine and Program Director of the Endocrinology and Metabolism Residency Training Program at McGill University. This conversation covers GLP-1 agonists, what they are, their uses, the media hype, new and exciting research, and some advice for trainees. The show notes include a glossary of terms, links to publications referenced in the episode, and a full transcript of our conversation.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.188 | 0.035 |
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".