Transcutaneous Electrical Nerve Stimulation in Patients With Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: Transcutaneous electrical nerve stimulation (TENS) has been reported to relieve pain and improve function in patients with knee osteoarthritis. The purpose of this systematic review and meta-analysis was to evaluate the efficacy of TENS for the management of knee osteoarthritis. METHODS: We searched Embase, PubMed, CENTRAL, SIGLE, PEDro, and clinicaltrials.gov, up to June 2014 for literature related to TENS used for the treatment of knee osteoarthritis. Two authors independently screened the searched records based on the title and abstract. Information including the authors, study design, mean age, sex, study population, stimulation frequency (of TENS), outcome measures, and follow-up periods were extracted by the 2 authors. RESULTS: Eighteen trials were included in the qualitative systematic review, and 14 were included in the meta-analysis. TENS significantly decreased pain (standard mean difference, -0.79; 95% confidence interval [CI], -1.31 to -0.27; P<0.00001) compared with control groups. There was no significant difference in the Western Ontario and McMaster Universities Osteoarthritis Index (standard mean differences, -0.13; 95% CI, -0.35 to 0.1; P=0.09) or the rate of all-cause discontinuation (risk ratio, 0.77; 95% CI, 0.48 to 1.22; P=0.94) between the TENS and control groups. DISCUSSION: TENS might relieve pain due to knee osteoarthritis. Further randomized-controlled trials should focus on large-scale studies and a longer duration of follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".