Scientific overview: CSCI-CITAC Annual General Meeting and Young Investigators' Forum 2014
Bibliographic record
Abstract
The Canadian Society of Clinician Investigators (CSCI) and Clinical Investigator Trainee Association of Canada/Association des cliniciens-chercheurs en formation du Canada (CITAC/ACCFC) annual general meeting (AGM) was held in Toronto during November 21-24, 2015 for the first time in conjunction with the University of Toronto Clinician-Investigator Program Research Day. The overall theme for this year's meeting was the role of mentorship in career development, with presentations from Dr. Chaim Bell (University of Toronto), Dr. Shurjeel Choudhri (Bayer Healthcare), Dr. Ken Croitoru (University of Toronto), Dr. Astrid Guttman (University of Toronto), Dr. Prabhat Jha (University of Toronto) and Dr. Sheila Singh (McMaster University). The keynote speakers of the 2014 AGM included Dr. Qutayba Hamid, who was presented with the Distinguished Scientist Award, Dr. Ravi Retnakaran, who was presented with the Joe Doupe Award, and Dr. Lorne Babiuk, who was the CSCI-RCPSC Henry Friesen Award winner. The highlight of the conference was, once again, the outstanding scientific presentations from the numerous clinician investigator (CI) trainees from across the country who presented at the Young Investigators' Forum. Their research topics spanned the diverse fields of science and medicine, ranging from basic science to cutting-edge translational research, and their work has been summarized in this review. Over 120 abstracts were presented at this year's meeting. This work was presented during two poster sessions, with the six most outstanding submitted abstracts presented in the form of 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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.100 | 0.081 |
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".