MJM MedTalks (S01E02): A Conversation with Dr. Shirin Enger
Bibliographic record
Abstract
Podcast Links: Spotify Anchor 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, MSc candidate and MJM Podcast Team member Nadia Blostein interviews Dr. Shirin A. Enger, medical physicist and Associate Professor at the Gerald Bronfman Department of Oncology & Medical Physics Unit. This interview focuses on the interdisciplinary and translational nature of medical physics research, the workflow of radiotherapy, and concludes with a list of open-source initiatives that Dr Enger’s group has participated in, including the McMed Hacks Series on Machine Learning and Medical Imaging. The show notes include a transcript of the podcast, a more detailed content overview, glossary of important terms and resources and references. This episode was recorded and edited by MJM Podcast Team member Nadia Blostein with input from the entire MJM Podcast Team and transcribed by Susan Wang.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.131 | 0.031 |
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".