Overview of The Canadian Clinician Investigator Trainees’ Research Presented at The 2020 CSCI-CITAC Joint Meeting
Bibliographic record
Abstract
The 2020 Annual General Meeting (AGM) 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 the first meeting to be hosted virtually. The theme was “Navigating Uncertainty, Embracing Change and Empowering the Next Generation of Clinician-Scientists”, and the meeting featured lectures and workshops that were designed to provide knowledge and skills for professional development of clinician investigator trainees. The opening remarks were given by Jason Berman (President of CSCI/SCRC), Tina Marvasti (President of CITAC/ACCFC) and Nicola Jones (University of Toronto Clinician Investigator Program Symposium Chair). Dr. Michael Strong, President of the Canadian Institutes of Health Research, delivered the keynote presentation titled “CIHR’s COVID-19 Response and Strategic Planning”. Dr. John Bell (University of Ottawa) received the CSCI Distinguished Scientist Award, Dr. Stanley Nattel (Université de Montréal) received the CSCI-RCPSC Henry Friesen Award (RCPSC; Royal College of Physicians and Surgeons of Canada) and Dr. Meghan Azad (University of Manitoba) received the CSCI Joe Doupe Young Investigator Award. Each scientist delivered talks on their award-winning research. The interactive workshops were “Developing Strategies to Maintain Wellness”, “Understanding the Hidden Curriculum: Power and Privilege in Science and Medicine”, “Hiring a Clinician Scientist Trainee: What Leaders Are Looking For” and “COVID-19: A Case Study for Pivoting Your Research”. The AGM included presentations from clinician investigator trainees nationwide. Over 70 abstracts were showcased, most are summarized in this review, and six were selected for oral presentations.
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 | 0.034 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.061 | 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".