Fitts's Law using lower extremity movement: A performance driven outcome measure for degenerative lumbar spinal stenosis
Bibliographic record
Abstract
A paucity of objective outcome measures exists for managing movement disorders, including degenerative Lumbar Spinal Stenosis (LSS). Application of Fitts' Law may provide a novel approach to clinical outcome measurement because task performance is resistant to learning and a range of performance ability can be measured by altering task difficulty. The present study compared LSS patients (N=9) to healthy age matched participants (N=7) on a lower limb aiming task to determine their respective motor performance characteristics. Participants performed pointing movements with their great toe to a series of squares that appeared on a touch screen monitor with 6 possible indices of difficulty (ID; 3 target distances and 2 target widths). Movements were recorded using an Optotrak 3D Investigator (300Hz). Reaction time (RT), movement time (MT) and peak velocity (PV) were analyzed using 2 Group (Healthy, LSS) by 6 ID (2, 3a, 3b, 4a, 4b, 5) mixed ANOVAs. A main effect for MT demonstrated a linear relationship with ID indicating Fitts' law was maintained. Overall, LSS patients demonstrated significantly faster RTs and slower MTs. A Group x ID interaction for PV revealed LSS patients did not scale movement execution to the same degree as healthy controls. Findings are discussed as they relate to Fitts' Law interpretation and clinical application. Future research will aid clinical incorporation ofmotor performance tasks as tools to establish functional ability.Acknowledgments: Direct support for this research was provided by the Alexander Gibson Fund, additional support provided by the Manitoba Health Research Council.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".