Peripheral Neuropathy in the Lower Limbs of Individuals With Spinal Cord Injury or Disease
Bibliographic record
Abstract
PURPOSE: This study investigated the frequency and types of peripheral neuropathy in the lower limbs of patients undergoing rehabilitation after traumatic spinal cord injury or spinal cord disease. METHODS: This study included consecutive patients with spinal cord injury/spinal cord disease who had electrophysiological assessments during their admission in a rehabilitation center from October 2015 to July 2019. Patients with traumatic spinal cord injury were compared with patients with nontraumatic spinal cord disease. RESULTS: There were 67 patients (52 male patients, 15 female patients; mean age = 56.5 yrs) of whom 36 patients had spinal cord injury and 31 patients had spinal cord disease. Most of the patients were middle-aged men with at least one preexisting medical comorbidity, who were mostly admitted for rehabilitation of cervical, incomplete spinal cord injury/spinal cord disease. Most patients (86.6%) had abnormal electrophysiological studies representing 5.57% of all admissions. A length-dependent polyneuropathy was diagnosed in 0.77% of all admissions (n = 8). The group of patients with spinal cord injury was comparable with the group of patients with spinal cord disease regarding the other baseline data, clinical, and electrophysiological findings. CONCLUSIONS: Diseases of the peripheral nervous system were similarly found among patients undergoing rehabilitation for either spinal cord injury or spinal cord disease. A length-dependent polyneuropathy was diagnosed in 0.77% of all admissions. Timely diagnosis and proper treatment of the cause of peripheral neuropathies in the lower limbs in these patients may potentially influence rehabilitation protocols and improve patient outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".