Fourth Annual Clinician Scientist Trainee Symposium at the Schulich School of Medicine & Dentistry
Bibliographic record
Abstract
The Annual Clinician Scientist Trainee Symposium (CSTS) gathers the local medical research community at the Schulich School of Medicine and Dentistry, including research trainees at the medical student, resident and fellow levels. Trainees showcase their current and upcoming work, and leaders in the community impart their perspectives on the importance and future of research in medicine. At the 4th Annual CSTS, perseverance and cautious optimism emerged as the characteristics that trainees should foster through their future careers as clinician scientists. This was echoed by the challenges and encouraging findings presented by trainees, who conducted their work as part of research projects within the MD, MD/PhD and clinical investigator programs. The four oral presentations and 10 three-minute thesis presentations covered the full breadth of medical disciplines across the spectrum of translational medicine, from fundamental sciences through knowledge translation. The CSTS concluded with the keynote presentation by Michael Strong, current president of the Canadian Institutes of Health Research, who gave a glimpse into his decades-long path of overcoming and overturning dogmatic views in the world of amyotrophic lateral sclerosis research, demonstrating how roadblocks in research can become an impulse for stronger science.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.107 | 0.039 |
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".