On becoming a clinician-scientist: The importance of mentorship and role models
Bibliographic record
Abstract
Brent graduated from medicine (with distinction) from the University of Alberta in 1984, and subsequently trained in Internal Medicine at the University of Toronto and in Critical Care at the University of Manitoba. He later did a post-doctoral fellowship in molecular/cellular biology at National Jewish Center (Denver, CO). His research interests now focus on examining diseases of the critically ill using metabolomics. When he finished training in 1996, Brent was recruited to the University of Calgary in Critical Care Medicine and is now a Professor of Medicine. In Calgary, he helped to establish the graduate program in Critical Care Medicine and is involved in training clinicians, scientists and clinician-scientists. Brent was President of Canadian Society for Clinical Investigators in 2011-2013 and has been a member of the Editorial Board of Clinical Investigative Medicine for over 10 years.
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.
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 | 0.159 | 0.362 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.049 | 0.037 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.016 | 0.044 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".