Scientific overview: CSCI – CITAC Annual General Meeting and Young Investigator’s Forum 2012
Bibliographic record
Abstract
In 2012, 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 of Clinician Investigators (CSCI) was held 19-21 September in Ottawa. Several globally-renowned scientists, including 2012 Friesen International Prize recipient, Dr. Marc Tessier-Lavigne, the CSCI/Royal College Henry Friesen Award recipient, Dr. Morley Hollenberg, and the recipient of the Joe Doupe Young Investigator Award, Dr. Phillip Awadalla, presented on a range of topics on research in basic and translational science in medicine. This year's CITAC Symposium featured presentations by Dr. Alain Beaudet, Dr. Michael Strong and Dr. Vivek Goel on the Role of Physician Scientists in Public Health and Policy, which was followed by a lively discussion on the role of basic science and clinical research in patient-oriented policy development. This scientific overview highlights the research presented by trainees at both the oral plenary and poster presentation sessions. As at previous meetings, research questions investigated by this year's trainees span multiple medical disciplines; from basic science to clinical research to medical education. Below is a summary of the presentations showcased at the Young Investigator's Forum.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.073 | 0.051 |
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".