The Contribution of Motor Commands to the Perturbations Induced by Sensorimotor Conflicts in Fibromyalgia
Bibliographic record
Abstract
Individuals with pain report higher sensory disturbances during sensorimotor conflicts compared to pain-free individuals. In the pain field, it is frequently assumed that disturbances arise from a discordance between sensory and efference copies (defined as sensory-motor conflict), while in the sensorimotor control field they are considered to result from the incongruence between sensory modalities (defined as sensory-sensory conflict). The general aim of this study was to disentangle the relative contribution of motor efferences and sensory afferences to the increased sensitivity to sensorimotor conflicts in individual with fibromyalgia (n = 20) compared to controls (n = 20). We assessed sensory and motor disturbances during sensory-sensory and sensory-motor conflicts using a robotized exoskeleton interfaced with a 2D virtual environment. There was a significant interaction between the group and the type of conflict (p = 0.03). Moreover, the increase in conflict sensitivity from sensory-sensory to sensory-motor conflicts in fibromyalgia was related to conflict-induced motor disturbances (r = 0.57; p < 0.01), but did not result from a poorer proprioception (r = 0.12; p = 0.61). Therefore, it appears that higher conflict sensitivity in fibromyalgia is mainly explained by a sensory-motor conflict rather by a sensory-sensory conflict. We suggest this arises due to a deficit in updating predicted sensory feedback rather than in selecting appropriate motor commands.
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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 | 0.004 |
| 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) | 0.001 | 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".