MJM MedTalks (S01E03): A Conversation with Dr. Ahmad Haidar
Bibliographic record
Abstract
McGill Journal of Medicine (MJM) MedTalks is a podcast series where members of the McGill Faculty of Medicine and Health Sciences are interviewed on topics related to career, research, advocacy and more. The aim of MedTalks is to open a space where faculty members can share information and advice for trainees in healthcare and medical sciences. In this episode, MJM Podcast Team members, McGill medical student Dylan Langburt and MSc candidate Khiran Arumugam interviews Dr. Ahmad Haidar about his work in the Artificial Pancreas Lab. Dr. Haidar is an assistant professor from the department of Biomedical Engineering, Faculty of Medicine and Health Sciences here at McGill. He leads an interdisciplinary research program that applies feedback control theory and mathematical modeling to diabetes, psychological, and clinical problems. Since 2011, Dr. Haidar’s research aim has been to develop and clinically test novel artificial pancreas systems with the use of Bayesian modeling and isotope tracers to study the pharmacokinetics and pharmacodynamics of dual-hormones (insulin and pramlintide). He is the first to develop the dosing algorithm of the artificial pancreas system. This interview will potentially cover concepts such as the artificial pancreas, diagnosis and treatment systems, automated delivery systems, diabetes (Type 1), biomedical devices, glucose-isotope tracers, glucose physiology metabolism, etc. Today’s conversation is divided into three parts: 1) Questions regarding the history from the discovery to the evolution of insulin; 2) Questions focusing on specific objectives in Dr. Haidar’s lab; 3) General advice for medical/research students. The show notes include a transcript of the podcast, a more detailed content overview, glossary of important terms and resources and references. This podcast is produced and edited by MJM’s social media team members Dylan and Khiran with input from the entire MJM Podcast Team. Please see our website www.mjmmed.com for more information, including a link to show notes.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.077 | 0.023 |
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".