Comparison of Stochastic vs. Conventional Transcutaneous Electrical Stimulation for Pain Modulation in Patients with Electromyographically Documented Radiculopathy
Bibliographic record
Abstract
OBJECTIVE: To determine if a transcutaneous electrical stimulation (TENS) unit modified to deliver electrical impulses at random (R) or stochastic frequency, called TENS-R, provided better pain relief than conventional TENS. DESIGN: A prospective, randomized, double-blinded, placebo-controlled study at an urban teaching hospital. A total of 13 adult subjects with radiculopathy on electromyogram and chronic radicular pain rated pain before and after walking 100 feet with proximal (axial) placement of TENS leads with randomized settings on conventional TENS, placebo, or TENS-R and, subsequently, with distal (limb) placement of TENS leads with randomized settings, all on the same day. The pain measures used were the McGill Pain Questionnaire, parts 1 and 2, and the Visual Analog Scale. The functional measure was speed of walking. RESULTS: Four men and seven women completed the study pain scores, measured by McGill Pain Questionnaire part 2, significantly improved when the patient used TENS-R vs. conventional TENS (P = 0.006, analysis of variance). Placement of TENS electrodes on the back significantly decreased pain compared with lead placement on the legs for McGill Pain Questionnaire part 1 (P = 0.007), McGill Pain Questionnaire part 2 (P = 0.042), and the Visual Analog Scale (P = 0.026) measures. CONCLUSIONS: Qualitative pain scores significantly improved when the patient used TENS-R vs. conventional TENS. Lead placement of any TENS modality over the back vs. over the leg improved all pain scores.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".