Efficacy of transcutaneous electrical nerve stimulation in people with pain after spinal cord injury: a meta-analysis
Bibliographic record
Abstract
STUDY DESIGN: Meta-analysis. OBJECTIVES: This study aimed to evaluate the effect of transcutaneous electrical nerve stimulation in people with pain after spinal cord injury by meta-analysis. METHODS: Reviewed PubMed, Embase, Cochrane library, as well as China National Knowledge Infrastructure (CNKI), Wanfang, and Vip databases to search the randomized controlled trials of pain after spinal cord injury through transcutaneous electrical nerve stimulation from the beginning of the library to March 2021, and analyze the literature with RevMan 5.3 software and the bias in the literature with STATA 12.0 software. RESULTS: There are six randomized controlled trials in the study with 165 cases. 83 cases in the test group were given transcutaneous electrical nerve stimulation, and 82 cases in the control group used sham stimulation or other treatments. Meta-analysis results showed the experimental group's visual analog scale (MD = -1.52, 95%CI, -2.44 to -0.60, P = 0.001) and short-form McGill pain questionnaire scores (MD = -0.70, 95% CI, -1.03 to -0.25, P = 0.002) were lower than those of the control group. CONCLUSIONS: Transcutaneous electrical nerve stimulation has some clinical therapeutic effects on persons with pain after spinal cord injury, but due to the lack of literature, the sample size is not large, and clinical trials need to be further improved later.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.064 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".