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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".