Bench2Bedside 2022 Case Competition & Conference: Translational Research Proposals
Bibliographic record
Abstract
The Bench2Bedside Case Competition & Conference is an undergraduate initiative which aims to further undergraduate student understanding of translational research. This initiative prompts students to examine clinical questions through a basic science lens while examining laboratory work from a clinical perspective. It promotes an understanding of how clinical research is conducted, effective ways of communicating cross-disciplinary research findings, how findings are critically evaluated, and productively applied to patient care. Students engage in a two round case competition involving an initial written research proposal stage, followed by an oral presentation stage. The case competition is based on a case study developed in conjunction with an authority in translational research. Experts with a rich understanding of translational research are also involved in judging the case competition, along with workshops conducted during the conference portion of the event. This year’s case study focused on neurocognitive disorder and COVID-19 in an elderly patient. The research proposal abstracts for competition finalists are highlighted in this abstract book. For more information on the competition structure, and conference offerings please visit https://bench2bedside.club/.
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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.399 | 0.188 |
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".