Overview of The Canadian Clinician Investigator Trainees’ Research Presented at CSCI-CITAC Joint Meeting
Bibliographic record
Abstract
The 2021 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 hosted virtually on November 14-16th, 2021. The theme of the AJM was "Communication, Collaboration, and Tools for the Next Generation of Clinician Scientists", and emphasized lectures, panels and interactive workshops designed to provide knowledge and skills for professional development of clinician investigator trainees. The opening remarks were given by Nicola Jones (President of CSCI/SCRC) and Adam Pietrobon (Past President of CITAC/ACCFC). The keynote speaker was Dr. Timothy Caulfield, who delivered the presentation titled "Communication in the Era of Misinformation". Dr. Michael Hill (University of Calgary) received the CSCI Distinguished Scientist Award and Dr. Philippe Campeau (Université de Montréal) received the CSCI Joe Doupe Young Investigator Award. Each of the scientists delivered award winning talks during the symposium titled "All the King's Horses and All the King's Men" and "Understanding Growth Plate Disorders to Better Treat Them", respectively. The three interactive workshops included "Data Visualization", "Science Communication on Social Media" and "Mentorship in Action". The two panels were "CIHR Engagement: Challenges and Opportunities in the Clinician Investigator Career Path" and "Early Career Investigator Panel". The AJM also included presentations from clinician investigator trainees from across the country. Over 60 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 | 0.028 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.067 | 0.015 |
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".