Hierarchical task ordering for time reduction on KINARM assessment protocol
Bibliographic record
Abstract
Advances in robotic technologies offer objective, highly reliable tools for assessment of brain function following stroke. KINARM is an exoskeleton device that uses a number of behavioral tasks to objectively quantify sensorimotor, proprioceptive and cognitive brain function. As more tasks are developed to more broadly assess different aspects of behavior using the robot, different strategies are required to reduce the overall assessment time. The present study investigates how non-linear hierarchical ordering theory can be applied to determine the ordering on a set of four tasks on the KINARM exoskeleton robot. Evaluation is based on task discretization, which determines whether an individual passes or fails a certain task on the robot. Results of the study suggest an ordering which determines the results of success or failure on a sensorimotor task for the unaffected arm of stroke survivors based on the assessment results of a ball drop object-hit task with 97% confidence. This can be used to reduce the assessment time by over eight minutes for a subgroup of stroke survivors compared to the current KINARM assessment protocol.
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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 | 0.022 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".