Self-reported Measures of Function Compared to Lower Limb Motor Performance in People With and Without Imaging Evidence of Unilateral Lumbar Nerve Root Compression: A Cross-sectional Study
Bibliographic record
Abstract
OBJECTIVE: The primary objective of the present study was to determine if imaging findings of unilateral lumbar nerve root compression (ULNRC) impact performance on a coordinated motor performance task and to determine if there were correlations between motor performance and self-reported clinical measures. METHODS: People with back pain (N = 45) were stratified into 3 groups based on combinations of: lumbar imaging; and clinical presentation for ULNRC. Group 1 included people with imaging of lumbar nerve root compression, who presented with neurological deficit. Group 2 people demonstrated imaging evidence of nerve compression, without motor, sensory or reflex change. Group 3 participants possessed only degenerative changes on lumbar imaging films, and were neurologically intact. Performance measures included behavioral and kinematic variables from an established lower limb Fitts' Task requiring movements to targets of different difficulties. Self-reported measures of disability, function and pain were collected. Analysis of variance for between and within group variables were conducted, and Pearson correlation compared performance with self-reported measures. RESULTS: All groups yielded main effects for movement time with increasing task difficulty as predicted by Fitts' Law. A main effect revealed Group 1 participants performed less accurately than Group 3 participants. Positive correlations were predominantly found between self-report measures and motor performance for Group 2 and Group 3. CONCLUSION: Imaging, and self-reported measures alone did not predict function, however, Fitts' task performance accuracy effectively differentiated groups.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".