Spatially defined disruption of motor imagery performance in people with osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: To determine whether motor imagery performance is disrupted in patients with painful knee OA and if this disruption is specific to the location of the pain. METHODS: Twenty patients with painful knee OA, 20 patients with arm pain and 20 healthy pain-free controls undertook a motor imagery task in which they made left/right judgements of pictured hands and feet. Accuracy and reaction time of judgements were compared between groups and pain locations (side: left vs right; site: upper vs lower). RESULTS: Patients with knee pain were less accurate (P < 0.01) than healthy controls, but not different from people with arm pain (all P > 0.11). There were no differences in reaction time between groups (P = 0.64). Further, there was no effect of side or site of pain on reaction time (P = 0.43, 0.54, respectively) and no effect of site of pain on accuracy of left/right judgements (P = 0.12). However, there was an interaction effect of side of pain on accuracy of left vs right images (P = 0.03). If left-sided pain was present, accuracy was lower when images showed left hands/feet than when images showed right hands/feet. CONCLUSION: Motor imagery performance is disrupted in patients with knee OA, but is also disrupted in patients with arm pain. Accuracy of left/right judgements is disrupted in a spatially defined manner, raising the important possibility that brain-grounded maps of peripersonal space contribute to the cortical proprioceptive representation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".