Neuropathic Pain Post Spinal Cord Injury Part 1: Systematic Review of Physical and Behavioral Treatment
Bibliographic record
Abstract
BACKGROUND: Neuropathic pain has various physiologic and psychosocial aspects. Hence, there is a growing use of adjunct nonpharmacological therapy with traditional pharmacotherapy to reduce neuropathic pain post spinal cord injury (SCI). OBJECTIVE: The purpose of this study was to conduct a systematic review of published research on nonpharmacological treatment of neuropathic pain after SCI. METHODS: MEDLINE, CINAHL, EMBASE, and PsycINFO databases were searched for articles addressing nonpharmacological treatment of pain post SCI. Articles were restricted to the English language. Article selection was conducted by 2 independent reviewers with the following inclusion criteria: the subjects participated in a treatment or intervention for neuropathic pain; at least 50% of the subjects had an SCI; at least 3 subjects had an SCI; and a definable intervention was being studied. Data extracted included study design, study type, subject demographics, inclusion and exclusion criteria, sample size, outcome measures, and study results. Randomized controlled trials (RCTs) were assessed for quality using the Physiotherapy Evidence Database (PEDro) assessment scale. Levels of evidence were assigned to each intervention using a modified Sackett scale. RESULTS: The 16 articles selected for this review fell into 1 of 2 categories of nonpharmacological management of pain after SCI: physical and behavioral treatments. The pooled sample size of all studies included 433 participants. Of the 16 studies included, 7 were level 1, 3 were level 2, and 6 were level 4 studies. CONCLUSIONS: Physical interventions demonstrated the strongest evidence based on quality of studies and numbers of RCTs in the nonpharmacological treatment of post-SCI pain. Of these interventions, transcranial electrical stimulation had the strongest evidence of reducing pain. Despite a growing body of literature, there is still a significant lack of research on the use of nonpharmacological therapies for SCI pain.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".