Scientific Overview: CSCI-CITAC Annual General Meeting and Young Investigators' Forum 2015
Bibliographic record
Abstract
The 2015 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 held in Toronto November 23-25, 2015, in conjunction with The University of Toronto Clinician Investigator Program Research Day. The theme for this year's meeting was "It takes a village" and the focus was the various support systems necessary to train a successful clinician scientist. The meeting featured an opening presentation by Dr. Vincent Dumez and workshops by Dr. Peter Nickerson, Dr. Jane Aubin, Dr. Kelly Warmington and Dr. Norman Rosenblum, and MD/PhD trainees Nardin Samuel, Kevin Wang and Kirill Zaslavsky. The keynote speakers were Dr. David Malkin (Hospital for Sick Children) who received the CSCI-RCPSC Henry Friesen Award, Dr. Brent Richards (McGill University) who received the Joe Doupe Award and Ernesto Shiffrin (Lady Davis Institute) who received the Distinguished Scientist Award. As always, the conference showcased outstanding scientific presentations from clinician investigator trainees from across the country at the Young Investigators' Forum. The research topics, which ranged from basic sciences to clinical medicine and translational work, are summarized in this review. Over 90 abstracts were presented at this year's meeting during two poster sessions, with several of the outstanding abstracts 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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.081 | 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".