Scientific overview on CSCI-CITAC Annual General Meeting and 2017 Young Investigators’ Forum
Bibliographic record
Abstract
The 2017 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 Annual General Meeting (AGM) held in Toronto, Ontario November 20-22, 2017, in conjunction with the University of Toronto Clinician Investigator Program Research Day. The theme for this year's meeting was "Roll up your sleeves-How to manage your physician scientist career", emphasizing lectures and workshops that were designed to provide tools for being proactive and successful in career planning. The keynote speakers were Dr. Rod McInnes (McGill University and Canadian Institutes of Health Research Acting President), who was the Distinguished Scientist Award recipient, Dr. David Goltzman (McGill University), who was the 2017 Henry Friesen Award recipient, Dr. Gillian Hawker (University of Toronto), Dr. Mike Sapieha (Université de Montréal), who was the 2017 Joe Doupe Award recipient, and Dr. Alex MacKenzie (Children's Hospital of Eastern Ontario Research Institute, University of Ottawa). The workshops, focusing on career development for clinician scientists, were hosted by Dr. Lisa Robinson, Dr. Nicola Jones, Kevin Vuong, Fran Brunelle, Dr. Jason Berman and Dr. Alan Underhill. Further to this, the Young Investigators' Forum encompasses presentations from scientist-clinician trainees from across the country. All scientific abstracts are summarized in this review. There were over 100 abstracts showcased at this year's meeting during the highlighted 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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.130 | 0.075 |
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".