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Record W2017071140 · doi:10.4103/0973-6131.113413

Additional effect of iyengar yoga and EMG biofeedback on pain and functional disability in chronic unilateral knee osteoarthritis

2013· article· en· W2017071140 on OpenAlexaboutno aff
Gopal Nambi, AmishaAtul Kumar Shah

Bibliographic record

VenueInternational Journal of Yoga · 2013
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMedicineWOMACPhysical therapyVisual analogue scaleBiofeedbackPhysical medicine and rehabilitationRehabilitationContractureInclusion and exclusion criteriaSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There are limited data about Iyengar Yoga and EMG biofeedback in knee OA, although the efficacy of EMG biofeedback in the rehabilitation of patients with quadriceps muscle weakness secondary to immobilization, contracture, and joint surgery has been well established. MATERIALS AND METHODS: Thirty subjects who have fulfilled inclusion and exclusion criteria were selected and divided into two groups (Group A and B). Both the groups were treated with EMG biofeedback, knee muscle strengthening exercises, and TENS. Group A received additionally Iyengar Yoga for 8 weeks. Both groups were evaluated by Visual Analog Scale and Modified WOMAC-Western Ontario McMaster Universities Scale. RESULTS: Patients in both groups experienced significant reduction in pain and improvement in functional ability. In VAS scale Group A showed reduction of 56.83% (P = 0.001) when compared with Group B 38.15% (P ≤ 0.001). In modified WOMAC Scale, Group A showed reduction of 59.21% (P = 0.001) when compared with Group B 34.08% (P = 0.001). CONCLUSION: Adding Iyengar Yoga along with conventional therapy provides better results in chronic unilateral knee osteoarthritis in terms of pain and functional disability.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.1670.000

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 head0.012
GPT teacher head0.283
Teacher spread0.271 · 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.

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

Citations19
Published2013
Admission routes1
Has abstractyes

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