Let’s Talk LMCC (S01E01): Glucose Abnormalities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Welcome to the McGill Journal of Medicine (MJM) 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 2 parts. In part one we provide an overview of the topic with the help of experts in the field, followed by Part 2 where we review LMCC styled questions to help consolidate knowledge. In this episode, we welcome our expert advisor, Dr. Alice Cheng, Endocrinologist and Associate Professor at the University of Toronto to speak on LMCC Objective 37-1* Glucose Abnormalities. This episode was written by Meryem Talbo and Dr. Alice Cheng, edited by Esther Kang, Katherine Lan and Susan Wang. Please see our website www.mjmmed.com for more information, including a link to show notes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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 it