Overview of the Canadian Clinician Investigator Trainees’ Research Presented at the 2022 CSCI-CITAC Joint Meeting
Bibliographic record
Abstract
The 2022 Annual Joint Meeting (AJM) and Young Investigators' Forum of the Canadian Society for Clinical Investigation / Société Canadienne de recherches clinique (CSCI/SCRC) and Clinician Investigator Trainee Association of Canada/Association des cliniciens-chercheurs en formation du Canada (CITAC/ACCFC) was held in Montréal, November 13-14, 2022. The theme of this year's AJM was "Strength in Perseverance" and focused on highlighting clinician-investigator trainee achievements and resilience in research engagement through recent challenging and unprecedented times. The opening remarks were given by Nicola Jones (president of CSCI/SCRC) and Heather Whittaker (past president of CITAC/ACCFC). The keynote speaker was Dr. Michael Strong, who delivered the presentation "The Future of Clinician Scientists in Canada." Dr. Caroline Quach (Université de Montréal) received the CSCI Distinguished Scientist Award and Dr. Amy Metcalfe (University of Calgary) received the CSCI Joe Doupe Young Investigator Award. Each of the clinician-scientists delivered presentations on their award-winning research. The four interactive workshops included "Social Media in Science and Medicine," "Diversity in Science and Medicine," "Running a Successful Research Program," and "Mentorship in Action." The AJM also included presentations from clinician investigator trainees from across the country. Over 90 abstracts were showcased at this year's meeting, most of which are summarized in this review. Six outstanding abstracts were selected for oral presentations during the President's Forum.
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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.058 | 0.014 |
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".