Transcutaneous Electrical Nerve Stimulation in Patients With Knee Osteoarthritis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it