Lower extremity Fitts' task performance by patients with degenerative lumbar spinal stenosis: The application of induced strain
Bibliographic record
Abstract
Fitts’ tasks are resistant to learning and have alterable task difficulty. The aforementioned combination makes Fitts’ tasks optimal for performance-based clinical outcome measure development. Degenerative lumbar spinal stenosis (LSS) patients report ambulation-induced strain. Our objective was to determine if a progressive exercise treadmill test (PETT) induces strain that impacts performance differentially than LSS presence. We compared LSS patients (N=16) and healthy individuals (N=16) on a lower-limb Fitts’ task before and after a 12-minute PETT. Participants performed great toe pointing movements to squares projected on a platform with 4 possible indices of difficulty (ID). An Optotrak 3D Investigator (300Hz) recorded movement. Reaction time (RT), movement time (MT), peak velocity (PV) and peak acceleration (PA) were analyzed using 2 Group (Healthy, LSS) by 2 Strain (pre PETT, post PETT) by 4 ID?(3, 4a, 4b, 5) mixed ANOVA models with planned comparisons on variables related to strain. Replicating our previous work, a Group x ID interaction for MT provided evidence that LSS participants were impacted more adversely by increased task difficulty. A Group x ID interaction for PV revealed LSS patients did not scale movement execution to the same degree as healthy controls. Planned comparisons indicated that induced strain facilitates reaction to Fitts’ task stimuli, for both groups, but that effect disappears for the LSS group at the most challenging ID. Findings are discussed related to Fitts’ Law interpretation, and clinical implications for performance-based outcome measure development and application. Acknowledgments: Support for this research was provided by a grant from the Manitoba Medical Service Foundation (MMSF). Special thanks to Ran Zheng for assistance with data collection.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".