Newsletter Fall 2018: Clinician Investigator Trainee Association of Canada (CITAC)
Bibliographic record
Abstract
Message from the President: Optimism for the Future The Clinician-Investigator Trainee Association of Canada (CITAC) was established in 2006 to address issues relevant to Canadian trainees seeking dual training in medicine and research. As clinician-investigator (CI) trainees, we comprise but a fraction (less than 5%) of all medical trainees. Our 'bilingual' careers render our individual paths less straightforward and more challenging. As a community, we have had to confront several disappointments, perhaps most notably the cessation of funding support for MD/PhD programs in 2015, previously offered by the Canadian Institutes of Health Research (CIHR). Despite these individual and collective challenges, I remain optimistic and incredibly excited about our future. In my own work, I am reminded constantly that being trusted with the dual responsibility of patient care and innovation in medicine is a privilege to be cherished, rather than a burden to be feared. That which makes our path doubly challenging also makes it doubly rewarding. The progress that CITAC has made over the years only adds to my optimism, and I wish to take this opportunity to remind you of how far we have come and how much further we hope to go.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.023 | 0.015 |
| Insufficient payload (model declined to judge) | 0.106 | 0.054 |
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".