Proceedings from the 6th Annual University of Calgary Leaders in Medicine Research Symposium
Bibliographic record
Abstract
On November 14, 2014, the Leaders in Medicine (LIM) program at the Cumming School of Medicine, University of Calgary hosted its 6th Annual Research Symposium. Dr. Danuta Skowronski, Epidemiology Lead for Influenza and Emerging Respiratory Pathogens at the British Columbia Centre for Disease Control (BCCDC), was the keynote speaker and presented a lecture entitled "Rapid response research during emerging public health crises: influenza and reflections from the five year anniversary of the 2009 pandemic". The LIM symposium provides a forum for both LIM and non-LIM medical students to present their research work, either as an oral or poster presentation. There were a total of six oral presentations and 77 posters presented. The oral presentations included: Swathi Damaraju, "The role of cell communication and 3D Cell-Matrix environment in a stem cell-based tissue engineering strategy for bone repair"; Menglin Yang, "The proteolytic activity of Nepenthes pitcher fluid as a therapeutic for the treatment of celiac disease"; Amelia Kellar, "Monitoring pediatric inflammatory bowel disease - a retrospective analysis of transabdominal ultrasound"; Monica M. Faria-Crowder, "The design and application of a molecular profiling strategy to identify polymicrobial acute sepsis infections"; Waleed Rahmani, "Hair follicle dermal stem cells regenerate the dermal sheath, repopulate the dermal papilla and modulate hair type"; and, Laura Palmer, "A novel role for amyloid beta protein during hypoxia/ischemia". 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 a previous issue of CIM (2014 37(5):E292) provides more details on the program. Briefly, 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 University of Calgary 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 were submitted 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.121 | 0.044 |
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".