Effects of Russian current and low-frequency pulsed current on discomfort level and current amplitude at 10% maximal knee extensor torque
Bibliographic record
Abstract
BACKGROUND: Low-frequency pulsed current (LFPC) and Russian current (RC), a type of kilohertz-frequency alternating current, have been frequently used for muscle strengthening in rehabilitation programs. Despite the popularity of these current types, it is unclear which is most effectively able to generate a similar submaximal torque with minimal discomfort and current amplitude. OBJECTIVE: To compare discomfort and current amplitude between LFPC and RC to achieve a knee extensor torque of 10% of the maximal isometric voluntary contraction (MIVC). METHODS: Twenty-two healthy subjects were subjected to three electrically elicited knee extensor submaximal contractions (10% MIVC) that were sustained for 10 seconds. The current amplitude required to achieve 10% MIVC and subjective discomfort were assessed directly by the electrical stimulator and with the Visual Analogue Pain Scale, respectively. A paired t-test was used to determine differences between the electrical currents (α = 0.05). RESULTS: LFPC required significantly lower current amplitude (15%) and a lower discomfort level (50%) to achieve 10% of MIVC compared to RC. CONCLUSION: LFPC current seems to be more effective than RC with respect to discomfort level and current amplitude to produce 10% of MIVC.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".