MJM MedTalks (S02E07+08): Global Ophthalmology: A Talk with Dr. Nathan Congdon
Bibliographic record
Abstract
McGill Journal of Medicine (MJM) MedTalks is a Podcast series where members of the medical and health science communities 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 a two-part episode, Samy Amghar, MJM Podcast Team member and second-year medical student at McGill University interviews guest-expert and global ophthalmologist Dr. Nathan Congdon. The first episode covers Dr. Congdon’s career, his experience practicing ophthalmology in low- and middle-income countries, and the importance of multidisciplinary teams in global health. The second episode focuses on some of Dr. Congdon’s research projects, including the ENGINE trials, discusses the future of global ophthalmology, and offers advice for trainees interested in ophthalmology and global health. The show notes include a glossary of terms, links to publications referenced in the episode, and a full transcript of our conversation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.134 | 0.032 |
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".