Proceedings from the 5th Annual University of Calgary Leaders in Medicine Research Symposium
Bibliographic record
Abstract
On November 8, 2013, the Leaders in Medicine (LIM) program hosted the 5th Annual Research Symposium. Dr. Jerrold Ellner, Chief of the Infectious Diseases section at Boston Medical Centre and Professor of Medicine at Boston University School of Medicine, was the keynote speaker and presented his lecture entitled “Tuberculosis Past, Present and Future”. The LIM symposium gives a forum for LIM as well as non-LIM medical students to present their research work as either an oral or poster presentation. There were a total of 53 abstracts presented and five oral presentations. The symposium was attended by over 100 students and more than 30 staff members. The oral presentations included • Amrita Roy, Aboriginal identity, ethnic minority status, and prenatal depressive symptoms in a longitudinal pregnancy cohort study in Alberta. • David Nicholl, Obstructive sleep apnea treatment with continuous positive airway pressure decreases intraglomerular pressure and alters renal sensitivity to angiotensin. • James Cotton, An assemblage A Giardia cathepsin B protease degrades interleukin-8 and attenuates neutrophil chemotaxis. • Krystyna Ediger, Alexander Arnold and Emily Shelton, Rebuilding the Calgary Student Run Clinic: A Model for Sustainability. • Sarah MacEachern, Inhibiting inducible nitric oxide synthase restores electrogenic ion transport in experimental IBD: a novel role for enteric glia. See the article on the University of Calgary Leaders in Medicine Program, “A Prescription that Addresses the Decline of Basic Science Education in Medical School” in this same issue of CIM for more details on the program. In short, the LIM Research Symposium has the following objectives: (1) to showcase the impressive variety of projects undertaken by students in the LIM Program as well as U of C medical students; (2) to encourage medical student participation in research and special projects; and, (3) to inform students and faculty about the diversity of opportunities available for research and special projects during medical school and beyond. The following abstracts are those that were put forward for publication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.110 | 0.033 |
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".