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Record W2122642085 · doi:10.1177/1759720x15575724

Efficacy of action potential simulation and interferential therapy in the rehabilitation of patients with knee osteoarthritis

2015· article· en· W2122642085 on OpenAlexaboutno aff
Bina Eftekharsadat, Arash Babaei-Ghazani, Afshin Habibzadeh, Babak Kolahi

Bibliographic record

VenueTherapeutic Advances in Musculoskeletal Disease · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlanarian Biology and Electrostimulation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisRehabilitationPhysical therapyPhysical medicine and rehabilitationAction (physics)Alternative medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: Knee osteoarthritis (OA) is the main cause of pain, physical impairment and chronic disability in older people. Electrotherapeutic modalities such as interferential therapy (IFT) and action potential simulation (APS) are used for the treatment of knee OA. In this study, we aim to evaluate the therapeutic effects of APS and IFT on knee OA. METHODS: In this randomized clinical trial, 67 patients (94% female and 6% male with mean age of 52.80 ± 8.16 years) with mild and moderate knee OA were randomly assigned to be treated with APS (n = 34) or IFT (n = 33) for 10 sessions in 4 weeks. Baseline and post-treatment Western Ontario and McMaster Universities Osteoarthritis (WOMAC) subscales, visual analogue scale (VAS) and timed up and go (TUG) test were measured in all patients. RESULTS: VAS and WOMAC subscales were significantly improved after treatment in APS and IFT groups (p < 0.001 for all). TUG was also significantly improved after treatment in APS group (p < 0.001), but TUG changes in IFT was not significant (p = 0.09). There was no significant difference in VAS, TUG and WOMAC subscales values before and after treatment as well as the mean improvement in VAS, TUG and WOMAC subscales during study between groups. CONCLUSION: Short-term treatment with both APS and IFT could significantly reduce pain and improve physical function in patients with knee OA.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.291
Teacher spread0.282 · 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 teacher head, 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

Citations29
Published2015
Admission routes1
Has abstractyes

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