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Effects of Russian Electrical Stimulation on Knee Osteoarthritis v1

2025· article· en· W4413465353 on OpenAlexaboutno aff
Ehsanur Rahman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsnot available
FundersJashore University of Science and Technology
KeywordsOsteoarthritisStimulationPhysical medicine and rehabilitationMedicinePhysical therapyInternal medicinePathology

Abstract

fetched live from OpenAlex

This protocol outlines a randomized controlled trial (RCT) to evaluate the effectiveness of Russian electrical stimulation–enhanced quadriceps strengthening exercise compared with strengthening exercise alone in 70 older adults with knee osteoarthritis, assessing pain, muscle strength, and functional ability over 6 months. The intervention consists of Russian electrical stimulation (50 Hz sinusoidal symmetric pulses, 400 μs pulse duration, 10 ms on/off bursts, 25% duty cycle: 5 s on / 20 s off) combined with a 6-week conventional quadriceps strengthening program. Outcomes will be measured using the VAS scale, pressure pain threshold via algometer, automated portable dynamometer, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the 6-Minute Walk Test. This study includes both genders, records baseline OA duration and severity, and evaluates multiple clinically relevant outcomes. It is expected to demonstrate that adding Russian electrical stimulation to strengthening exercise significantly improves pain, muscle strength, and particularly functional outcomes, with sustained benefits across gender limitations.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0260.002

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.

Opus teacher head0.003
GPT teacher head0.204
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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