Tactile acuity is disrupted in osteoarthritis but is unrelated to disruptions in motor imagery performance
Bibliographic record
Abstract
OBJECTIVE: To determine whether tactile acuity is disrupted in people with knee OA and to determine whether tactile acuity, a clinical signature of primary sensory cortex representation, is related to motor imagery performance (MIP; evaluates working body schema) and pain. METHODS: Experiment 1: two-point discrimination (TPD) threshold at the knee was compared between 20 participants with painful knee OA, 20 participants with arm pain and 20 healthy controls. Experiment 2: TPD threshold, MIP (left/right judgements of body parts) and usual pain were assessed in 20 people with painful knee OA, 17 people with back pain and 38 healthy controls (20 knee TPD; 18 back TPD). RESULTS: People with painful knee OA had larger TPD thresholds than those with arm pain and healthy controls (P < 0.05). TPD and MIP were not related in people with knee OA (P = 0.88) but were related in people with back pain and in healthy controls (P < 0.001). Pain did not relate to TPD threshold or to MIP (P > 0.15 for all). CONCLUSION: In painful knee OA, tactile acuity at the knee is decreased, implying disrupted representation of the knee in primary sensory cortex. That TPD and MIP were unrelated in knee OA, but related in back pain, suggests that the relationship between them may vary between chronic pain conditions. That pain was not related to TPD threshold nor MIP suggests against the idea that disrupted cortical representations contribute to the pain of either condition.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".