Newsletter Spring 2018: Clinician Investigator Trainee Association of Canada (CITAC)
Bibliographic record
Abstract
A decade of CITAC Annual General Meetings: 2007-2017 In 2007, the Clinician Investigator Trainee Association of Canada (CITAC) launched its inaugural Annual General Meeting (AGM). The AGM has since become a major annual event, jointly organized by CITAC and leaders from the Canadian Society for Clinical Investigation (CSCI), and continues to provide a forum for clinician investigator (CI) trainees to exchange ideas, advance career prospects and engage with the broader community. Indeed, since its inception, all Canadian institutions with medical doctor and clinician investigator (MD+CI) training programs have participated in the AGM, while more than 1,000 trainees have registered as CITAC members. The 10th CITAC-CSCI AGM was recently held in Toronto (November 20-22, 2017). There were nearly 200 attendees, including CI trainees, faculty member and physician leaders from across Canada (Figure 1A, 1B). Trainees spanning diverse career stages had opportunities to participate in interactive poster sessions, workshops and lectures by leading physician-scientists. These exercises were designed to encourage and enhance networking, career development and mentorship for prospective physician-scientists.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.211 | 0.088 |
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".