Effects of Pain and Pain Management on Motor Recovery of Spinal Cord–Injured Patients
Bibliographic record
Abstract
Background Approximately 60% of patients suffering from acute spinal cord injury (SCI) develop pain within days to weeks after injury, which ultimately persists into chronic stages. To date, the consequences of pain after SCI have been largely examined in terms of interfering with quality of life. Objective The objective of this study was to examine the effects of pain and pain management on neurological recovery after SCI. Methods We analyzed clinical data in a prospective multicenter observational cohort study in patients with SCI. Using mixed effects regression techniques, total motor and sensory scores were modelled at 1, 3, 6, and 12 months postinjury. Results A total of 225 individuals were included in the study (mean age: 45.8 ± 18 years, 80% male). At 1 month postinjury, 28% of individuals with SCI reported at- or below-level neuropathic pain. While pain classification showed no effect on neurological outcomes, individuals administered anticonvulsant medications at 1 month postinjury showed significant reductions in pain intensity (2 points over 1 year; P < .05) and greater recovery in total motor scores (7.3 points over 1 year; P < .05). This drug effect on motor recovery remained significant after adjustment for injury level and injury severity, pain classification, and pain intensity. Conclusion While initial pain classification and intensity did not reveal an effect on motor recovery following acute SCI, anticonvulsants conferred a significant beneficial effect on motor outcomes. Early intervention with anticonvulsants may have effects beyond pain management and warrant further studies to evaluate the therapeutic effectiveness in human SCI.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".