The Effect of Transcutaneous Electrotherapy on Lumbar Range of Motion and Paraspinal Muscle Characteristics in Chronic Low Back Pain Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Chronic low back pain (CLBP) affects paraspinal muscle size, quality (e.g., fatty infiltration), range of motion (ROM), and strength. Although transcutaneous electrotherapies are used to treat CLBP, their effects on paraspinal-related outcomes are not fully known. The aim of this systematic review and meta-analysis was to assess the overall effect of transcutaneous electrotherapies on trunk/lumbar ROM, paraspinal muscle morphology, and trunk muscle function (including strength and endurance) in CLBP patients. A systematic search of four databases and two study registers was conducted between 1 February 2022 and 15 September 2022. Two reviewers were responsible for screening and data extraction. Of the 3939 independent records screened, 10 were included in the systematic review and 2 in the meta-analysis. The results suggest there is limited evidence that both EMS and EMS plus exercise are superior to passive and active controls, respectively, for improving trunk muscle endurance. There is limited evidence that neither TENS nor mixed TENS are superior to controls for improving trunk muscle endurance. There is limited evidence that NMES is superior to passive controls for improving trunk muscle strength. The effect of transcutaneous electrotherapy on the other investigated outcomes was inconclusive. Future transcutaneous electrotherapy studies should focus on paraspinal-based outcomes that are under-studied.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".