Scientific Overview on CSCI-CITAC Annual General Meeting and 2016 Young Investigators' Forum
Bibliographic record
Abstract
The 2016 Annual General Meeting 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 a national conference held in Toronto November 21-23, 2016, in conjunction with The University of Toronto Clinician Investigator Program Research Day. The theme for this year's meeting was "Mapping Your Career as a Clinician-Scientist"; emphasizing essential skills for developing a fruitful career as clinician-scientist. The meeting featured an opening presentation by Dr. Alan Underhill, Dr. Nicola Jones and Alexandra Kuzyk. The keynote speakers were Dr. Nada Jabado (McGill University), who discussed the association between cancer and histones, Dr. Norman Rosenblum (University of Toronto), who addressed the career path and the "calling" of the Clinician Scientist, Dr. Martin Schmeing (McGill University), who was the 2016 Joe Doupe Award recipient, and Dr. Linda Rabeneck (Cancer Care Ontario and University of Toronto), who received the Friends of CIHR lectureship. The workshops, focusing on career development for clinician scientists, were hosted by Drs. Alan Underhill, Nicola Jones, Lynn Raymond, Michael Schlossmacher and Norman Rosenblum, as well as University of Toronto communication specialists, Caitlin Johannesson and Suzanne Gold. In addition, the Young Investigators' Forum included presentations from clinician investigator trainees from across the country. The research topics were diverse and comprehensive: from basic sciences to clinical practice; from epidemiology to medical engineering. All scientific abstracts are summarized in this review. Over 70 abstracts were showcased at this year's meeting during two poster sessions, 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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.100 | 0.064 |
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".