R2Play development: Fostering user-driven technology that supports return-to-play decision-making following pediatric concussion
Bibliographic record
Abstract
Objective To design a multi-domain return-to-play assessment system (R2Play) for youth athletes with concussion. Methods The R2Play system was developed using an overarching user-centered approach, the Design Thinking Framework, and research activities included: 1) structured brainstorming within our research team, 2) interviews with clinician and youth sports coaches, 3) building a testable prototype, and 4) interface testing through cognitive walkthroughs with clinician partners. Results Clinician and coach participants provided feedback on the R2Play concept, which was integrated into the design process and provided future directions for research. Examples of feedback-driven design choices included reducing assessment time, increasing ecological validity by adding in background noise, and developing youth-friendly graphical results screens. Following refinement based on stakeholder feedback, the R2Play system was outlined in detail and a testable prototype was developed. It is made up of two parts: a clinician tablet, and a series of tablet “buttons” that display numbers and letters. Youth athletes run between the buttons to connect a “trail” in ascending alphanumeric order, 1-A-2-B, etc. Their performance across a series of levels of increasing difficulty is logged on the clinician tablet. Initial testing with five clinicians showed the system's interface to have excellent usability with a score of 81% (SD = 8.02) on the System Usability Scale. Conclusion Through this research, a prototype of the R2Play system was innovated and evaluated by clinician and coach stakeholders. Initial usability was excellent and directions for future iterations were highlighted. Outcomes suggest the potential benefits of using technologies to assist in complex clinical assessment, as well as utilizing a user-centered approach to design.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".