Combining the Love of Art, Science, Business and Medicine to Innovate and Enhance Patient Health. Highlights from the 7th Annual Leaders in Medicine Symposium of the Cummings School of Medicine, Calgary, AB
Bibliographic record
Abstract
The Leader in Medicine (LIM) Program of the Cumming School of Medicine, University of Calgary, hosted its 7th Annual LIM Research Symposium on October 30, 2015 and participation grew once again, with a total of six oral and 99 posters presentations! Over 45 of our Faculty members also participated in the symposium. This year's LIM Symposium theme was "Innovations in Medicine" and the invited guest speaker was our own Dr. Breanne Everett (MD/MBA). She completed her residency in plastic surgery at University of Calgary and holds both a medical degree and an MBA from the University of Calgary. In her inspiring talk, entitled "Marrying Business and Medicine: Toe-ing a Fine Line", she described how she dealt with a clinical problem (diabetic foot ulcers), came up with an innovation that optimized patient care, started her own company and delivered her product to market to enhance the health of the community. She clearly illustrated how to complete the full circle, from identifying a clinical problem to developing and providing a solution that both enhances clinical care and patient health as well as reduces health care costs and hospital admissions. The research symposium was an outstanding success and the abstracts are included in companion article in CIM.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".