Outcomes Rather than Outputs: Collaborative Closed-Loop Design and Commercialization
Bibliographic record
Abstract
This paper outlines the Toronto Rehabilitation Institute Technology Team’s vision for translational research. The objective of the Technology Team is to help people age successfully by providing tools to manage the disabilities that come with aging. To facilitate this translational research and realizing real world benefits, the Technology Team has developed a collaborative closed-loop design process. We describe the five strategies that make up our approach. The strategies are: 1) Having a collaborative team of clinicians, technical experts, researchers and students; 2) Maintaining prototyping facilities on-site; 3) Using simulators to quickly, safely, and repeatably test ideas with the target population; 4) Building relationships with stakeholders; 5) Careful documentation in preparation of regulatory approvals. Together these strategies have helped our team focus on translating research findings into practical outcomes as the ultimate goal of our research. These outcomes include changes to policy and clinical practice as well as the creation of new products, in addition to the traditional focus of academic research groups on outputs such as publications and grants. Keywords: Commercialization, design, innovation, research and development, testing.
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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 | 0.169 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".