Let’s Talk LMCC (S01E03): Legal, Ethical and Organizational Aspects of Medicine - Consent, Truth Telling and Negligence
Bibliographic record
Abstract
Welcome to the McGill Journal of Medicine (MJM) Let’s Talk LMCC Review. This podcast series was created to aid medical students studying for the Canadian Medical Council (MCC)’s licensing exam. Each episode is created based on specific LMCC objectives and is divided into two sections. In Section 1 we provide an overview of the topic with the help of experts in the field, followed by Section 2 where we review LMCC styled questions to help consolidate knowledge. In this episode, we welcome our expert advisor, Dr. Carolyn Ells, a recently retired Associate Professor in the Department of Medicine at McGill, based at the Biomedical Ethics Unit, to speak on LMCC Objectives 121-1 Consent, 121-2 Truth Telling, and 121-3 Negligence. This episode was written by MJM Podcast Team members Amanda Sears and Esther Kang and Dr. Carolyn Ells.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.169 | 0.063 |
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".