Virtual Reality-Induced Sensorimotor Conflict Evokes Limb-Specific Sensory Disturbances in Complex Regional Pain Syndrome
Bibliographic record
Abstract
The origin of sensory disturbances in complex regional pain syndrome (CRPS) remains unclear. It has been hypothesized that such disturbances are due to attentional effects and/or sensorimotor integration deficits. If sensory disturbances are explained by sensorimotor integration deficits, they would be expected to be specific in terms of the category of sensation evoked and in terms of localization. Objective 1: To test whether sensory disturbances evoked by a unilateral sensorimotor conflict are specific to the painful limb and differ according to the category of sensory disturbances in individuals with a unilateral CRPS compared to healthy controls (HC). Objective 2: To assess the association between clinical characteristics and conflict-induced sensory disturbances. Objective 3: To assess conflict-induced motor disturbances. Ten adults with upper limb (UL) CRPS and 23 HC were recruited. Sensorimotor conflict was elicited with a KINARM exoskeleton interfaced with a 2D virtual environment allowing the projection of a virtual UL that was moving in either a congruent or incongruent manner relative to the actual UL movement. Participants rated sensory disturbances from 0 (no change) to 3 (high change) on a 8-item questionnaire. Items were classified into two Categories (Category 1: pain, discomfort, the feeling of losing a limb, change in weight and temperature; Category 2: feelings of peculiarity, the impression of gaining a limb and losing control). Motor disturbances were quantified as mediolateral drift and changes in amplitude of UL movement. Clinical characteristics included the intensity and duration of pain, proprioception, and body perception. CRPS participants report higher Category 1 than Category 2 disturbances for the Affected limb (while the reverse was observed for HC and for the Unaffected limb). In addition, no difference was observed between the Unaffected limb in CRPS and the Dominant limb in HC for Category 2 disturbances, while higher conflict sensitivity was observed for Category 1 disturbances. Conflict sensitivity was only related to higher pain for Category 1 disturbances in the Affected limb. Finally, no effect on motor disturbances was observed. While they do not completely rule out the attentional hypothesis, these results support the hypothesis of sensorimotor integration deficits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".