Scientific Overview of the CSCI-CITAC 2009 Conference
Bibliographic record
Abstract
From September 21st-23rd 2009, 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), held their annual conference in Ottawa. Participants included clinician investigators and trainees from across the country. The conference featured many excellent guest speakers including this year's recipient of the Henry G. Friesen International Prize in Health Research, Sir John Bell. There were several forums focusing on professional development, with topics such as "sustaining the clinician investigator in Canada", "succeeding as a clinician investigator", and "collaborating internationally with MD+ trainees", alongside networking opportunities to help establish relationships with potential mentors and collaborators. Further, the CSCI-CITAC annual conference featured some of the cutting edge research that MD+ trainees throughout Canada are engaged in. Trainees presented their research either at the Young Investigators Forum poster session or at the oral plenary. This scientific overview aims to highlight some of the research presented by trainees at the annual conference. The broad themes of scientific interest included topics from both basic science and clinical research. In this article, we summarize some of the major research questions that are being investigated by clinician-investigator trainees in the following areas: neurological sciences, cell biology, medicine, immunology, obstetrics, gynecology, neonatology, orthopedics, rheumatology, and public health.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.025 |
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".