Overview of the Canadian Clinician Investigator Trainees’ Research Presented at the 2024 CSCI-CITAC Joint Meeting
Bibliographic record
Abstract
The 2024 Annual Joint Meeting (AJM) and Young Investigators’ Forum of the Canadian Society for Clinical Investigation (CSCI) and Clinician Investigator Trainee Association of Canada (CITAC) was held on April 11, 2024, in Vancouver, British Columbia. Hosted in collaboration with the University of British Columbia (UBC) and the International Congress on Academic Medicine (ICAM), this meeting marked a significant opportunity for clinician investigator trainees to present their research and connect with national and international peers. The event included 70 trainees, consisting of 58 MD+ (MD/PhD, MD and PhD, MD–MSc, MD and MSc) and 12 Clinician Investigator Program and/or Surgeon-Scientist Training Program trainees. The keynote speaker, Dr. Marco Marra, delivered a presentation titled “From C. elegans Genetics to Precision Cancer Genomic Medicine, via the Human Genome Project: Reflections on a Collaborative Scientific Journey.” Dr. Kenneth Rockwood (Dalhousie University) received the CSCI Distinguished Scientist Award, and Dr. Bertrand Routy (Université de Montréal) received the CSCI Joe Doupe Young Investigator Award. Over 60 abstracts were showcased, most of which are summarized in this review, and six were selected for oral presentations. Following the AJM, trainees participated in ICAM's three-day health research program, where three AJM attendees were recognized among the top four presenters and were invited to the 2024 Canada Gairdner Awards Gala, with one of our AJM attendees winning the competition and being named the next Lindau Nobel Laurate Meetings Nominee.
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 | 0.025 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.016 |
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".