Scientific Overview on CSCI-CITAC Annual General Meeting and 2018 Young Investigators’ Forum
Bibliographic record
Abstract
The 2018 Annual General Meeting (AGM) and Young Investigators’ Forum (YIF) of the Canadian Society of Clinician Investigators (CSCI) and Clinician Investigator Trainee Association of Canada/Association des Cliniciens-Chercheurs en Formation du Canada (CITAC/ACCFC) was held in Toronto, Ontario on November 19–20, 2018, in conjunction with the University of Toronto Clinician Investigator Program Research Day. The theme for the meeting was “Prepare for Success—Things to Master Now for Clinician Scientists in Training”; with lectures and workshops that were designed to provide knowledge and hands-on skills to navigate life as a clinician investigator. The opening remarks were by Jason Berman (President of CSCI), Josh Abraham (President of CITAC/ACCFC) and Nicola Jones (University of Toronto Clinician Investigator Symposium Chair). The keynote speakers were Dr. Ruth Ann Marrie (University of Manitoba), who received the Distinguished Scientist Award, Dr. Davinder Jassal (University of Manitoba), who received the CSCI-RCPSC Henry Friesen Award, and Dr. Aleixo Muise (University of Toronto), who received the Joe Doupe Young Investigator Award. Dr. Minna Woo (University of Toronto), Canada Research Chair in Diabetes Signal Transduction, delivered the keynote lecture “From Onion Cells to Single Cell Seq—A Constant Change in Lenses: A perspective of an evolving clinician scientist”. The workshops, focusing on career development for clinician-scientists, were hosted by Drs. Robert Chen, Stephen Juvet, Lorraine Kalia, Phyllis Billia, Neil Goldenberg, Nicola Jones, Srdjanaa Filipovic, Jason Berman, Josh Abraham, Melanie Szweras, Joseph Ferenbok and Uri Tabori. The AGM also included presentations from clinician investigator trainees from across the country, and these abstracts are summarized in this review. Over 80 abstracts were showcased at this year’s meeting during the poster session, with six outstanding abstracts 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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.113 | 0.073 |
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".