Using Accelerometers to Score a Multi-domain Return-to-play Assessment for Youth Post-concussion
Bibliographic record
Abstract
Clinical guidelines recommend using multi-domain assessments to determine when youth may return-to-play after a concussion. However, multi-domain assessments are rarely used as they are difficult for clinicians to administer and score. This thesis describes the ideation, development, and evaluation of a system to identify movements-of-interest during a return-to-play assessment for youth (R2Play) with the aim of reducing clinician cognitive load. The system uses a low-cost accelerometer, a threshold-based algorithm, and two classifiers to automatically log initiation times, movement errors, and hesitations. To facilitate development, data was collected from 15 adults performing tasks simulating these movements. The developed algorithms achieved an ICC of 0.892±0.064 for initiation time, and F1 scores of 0.621±0.148 and 0.422±0.224 for hesitations and movement errors. Preliminary validation was performed on youth athletes (n=3) during R2Play. The system developed in this research is a step toward consistent scoring and lowering clinician cognitive load in multi-domain assessments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".