The effect of neuropathic pain treatments on pain interference following spinal cord injury: A systematic review
Bibliographic record
Abstract
Context Neuropathic pain is a common and debilitating condition following SCI. While treatments for neuropathic pain intensity have been reviewed, the impact on pain interference has not been summarized.Objective To systematically review the effect of neuropathic pain interventions on pain interference in individuals with spinal cord injury.Methods This systematic review included randomized controlled trials and quasi-experimental (non-randomized) studies which assessed the impact of an intervention on pain interference in individuals with spinal cord injury and neuropathic pain. Articles were identified by searching MEDLINE (1996 to April 11, 2022), EMBASE (1996 to April 11, 2022), PsycInfo (1987 to April, week 2, 2022). Studies were assessed for methodologic quality using a modified GRADE approach and were given quality of evidence (QOE) scores on a 4-point scale ranging from very low to high.Results Twenty studies met the inclusion criteria. These studies fell into the following categories: anticonvulsants (n = 2), antidepressants (n = 1), analgesics (n = 1), antispasmodics (n = 1), acupuncture (n = 2), transcranial direct current stimulation (n = 1), active cranial electrotherapy stimulation (n = 2), transcutaneous electrical nerve stimulation (n = 2), repetitive transcranial magnetic stimulation (n = 1), functional electrical stimulation (n = 1), meditation and imagery (n = 1), self-hypnosis and biofeedback (n = 1), and interdisciplinary pain programs (n = 4).Conclusion When considering studies of moderate to high quality, pregabalin, gabapentin, intrathecal baclofen, transcranial direct current stimulation, and transcutaneous electrical nerve stimulation (in 1 of 2 studies) were shown to have beneficial effects on pain interference. However, due to the low number of high-quality studies further research is required to confirm the efficacy of these interventions prior to recommending their use to reduce pain interference.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".