Lower limb Fitts' task motor performance in patients with and without imaging evidence of unilateral lumbar nerve root compression
Bibliographic record
Abstract
Shelley Sargent1, Steven Passmore1,2 1College of Rehabilitation Sciences, University of Manitoba 2 Faculty of Kinesiology & Recreation Management, University of Manitoba The present experiment sought to determine if diagnostic imaging findings of nerve root compression could predict lower limb Fitts' task performance. Patients presenting with back, and or leg pain to a surgical screening spine assessment clinic (N=45) were recruited and stratified into 3 groups based on clinical presentation: 1) positive imaging, positive neurological deficit; 2) positive imaging, negative neurological deficit; and 3) negative imaging, negative neurological deficit. Each group performed great toe pointing movements to squares projected on a platform with 4 possible indices of difficulty (ID). An NDI 3D Investigator (300Hz) recorded all movement in the sagittal plane. Movement time (MT), reaction time (RT), peak velocity (PV), peak acceleration (PA), time to peak velocity (ttPV) and time to peak acceleration (ttPA) were analyzed using 3 Group (1,2,3) by 4 ID (3,4a,4b,5) ANOVA models. Performance variables were also compared to traditional questionnaire-based clinical outcome measures. Main effects for ID were found for MT, PV, PA, ttPV and ttPA. No group, or group by ID interactions were found. Pearson's Correlation analysis revealed significant associations between self-report measures and motor performance variables for Group 2. Positive correlations were found for effected limb MT and the Oswestry Disability; effected limb MT and the Roland Morris Disability Questionnaire; and non-effected limb ttPA and non-effected limb Quadruple Numeric Pain Rating Scale (QNRS). A negative correlation was found for effected limb RT and the effected limb QNRS. Findings are discussed related to Fitts' Law interpretation, and clinical implications for performance-based outcome measure application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".