Chronic pain and sensorimotor deficits following peripheral nerve injury
Bibliographic record
Abstract
Following upper limb peripheral nerve transection and surgical repair (PNIr) patients frequently exhibit sensory and motor deficits, but only some develop chronic neuropathic pain. Thus, the sensorimotor outcome of PNIr may be impacted by individual factors. Therefore, our aims were to determine if patients with chronic neuropathic pain (PNI-P) following PNIr (1) are distinguished from patients without pain (PNI-NP) and healthy controls (HCs) by the psychological factors of pain catastrophizing, neuroticism or extraversion, and (2) exhibit more severe sensorimotor deficits than patients who did not develop chronic pain (PNI-NP). Thirty-one patients with complete median and/or ulnar nerve transection (21 PNI-NP, 10 PNI-P) and 21 HCs completed questionnaires to assess pain characteristics, pain catastrophizing, neuroticism and extraversion and underwent sensorimotor evaluation. Nerve conduction studies revealed incomplete sensorimotor peripheral recovery based on abnormal sensory and motor latency and amplitude measures in transected nerves. The patients also had significant deficits of sensory function (two-point discrimination and vibration, touch, and warmth detection), sensorimotor integration, and fine motor dexterity. Compared to PNI-NP patients, PNI-P patients had higher vibration detection thresholds, performed worse on sensory-motor integration tasks, had greater motor impairment, and showed more impaired nerve conduction. Furthermore, PNI-P patients had reduced cold pain tolerance, elevated pain intensity and unpleasantness during the cold pressor test, and they scored higher on neuroticism and pain-catastrophizing scales. These data demonstrate that chronic neuropathic pain following PNIr is associated with impaired nerve regeneration, profound sensorimotor deficits and a different psychological profile that may be predictive of poor recovery after injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".