The University of Ottawa Healthcare Symposium (UOHS) 2025 Pitch-O-Rama: Undergraduate Elevator Pitch Research Competition
Bibliographic record
Abstract
The University of Ottawa Healthcare Symposium (UOHS) is an undergraduate conference that highlights the role of interdisciplinary collaboration in healthcare. Now in its fifteenth year, UOHS brings together students and researchers from fields such as biomedical sciences, engineering, health policy, and digital health to explore current challenges and advancements in healthcare. The conference provides a platform for discussions on emerging technologies, healthcare delivery, and the integration of research into clinical practice. As part of the symposium, Pitch-O-Rama challenges students to present their research in a concise and engaging way. Participants explain the significance of their work in a short elevator pitch, demonstrating how their research contributes to healthcare innovation. Judges evaluate presentations based on clarity, originality, interdisciplinary impact, scientific rigour, and the ability to address a meaningful knowledge gap. The competition allows students to practice effective science communication and consider how their work connects with broader healthcare challenges. This abstract book features the top submissions from the 2025 competition, highlighting research from undergraduate students across various disciplines. For more details about UOHS and Pitch-O-Rama, please visit https://www.uohs-csuo.com/.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.215 | 0.059 |
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".