Scientific overview: CSCI-CITAC annual general meeting and young investigator’s forum 2010
Bibliographic record
Abstract
In 2010, the annual general meeting of the Clinical Investigator Trainee Association of Canada - Association des cliniciens-chercheurs en formation du Canada (CITAC-ACCFC) and the Canadian Society for Clinician Investigators (CSCI) was held between September 20 and 22 in Ottawa. Several globally-renowned scientists, including this year's CSCI/Royal College Henry Friesen Award recipient, Dr. Paul Kubes, Distinguished Scientist Award recipient, Dr. Gideon Koren and Joe Doupe Young Investigator Award recipient, Dr. Torsten Neil, discussed a variety of topics relating to the role of technology in medicine. The meeting was well attended by clinician scientists and trainees from across Canada and offered trainees mentorship and networking opportunities in addition to showcasing their research at the young investigator forum. The aim of this scientific overview is to highlight the research presented by trainees at both the oral plenary session as well as the poster presentation sessions of this meeting. Similar to last year's meeting [1], research questions being investigated by trainees covered the spectrum of medical disciplines, encompassing both basic science as well as clinical areas, and are summarized below.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.019 |
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".