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Record W4407044825 · doi:10.1163/17552559-00001106

Kinetic chain exercises on muscle cross-sectional area, pain, and functional mobility in knee osteoarthritis: A single-blinded, randomised multigroup clinical trial

2025· article· en· W4407044825 on OpenAlexaboutno aff
Manoj Kumar Tiwari, Nilima Vaidya, Shagun Agarwal

Bibliographic record

VenueComparative Exercise Physiology · 2025
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACOsteoarthritisMedicinePhysical therapyPhysical medicine and rehabilitationKnee pain

Abstract

fetched live from OpenAlex

Abstract The purpose of this study is to compare the effectiveness of three modes of kinetic chain exercises (KCE) on muscle cross-sectional area, pain, function, and mobility among individuals with knee osteoarthritis (IKOA). 60 IKOA aged, over 50 years were recruited using a convenience sampling technique based on selection criteria. Then, the group was randomly allocated into three different groups, namely, open kinetic chain exercise (OKCE), close kinetic chain exercise (CKCE), and combined chain exercise (CCE). Muscle cross-sectional area, pain, and functional mobility were measured using thigh girth measurement, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and timed-up-go test (TUG), respectively. Data obtained before (baseline), at the end of the 3rd and 6th weeks were taken for analysis. Statistical significant ( ) changes were observed in thigh girth measurement, WOMAC and TUG in CCE than OKC and CKC groups. The result of this study suggests that CCE are more effective in improving functional outcomes and mobility than OKCE and CKCE in PKOA. Registered with Clinical Trials Registry, India (CTRI): REF/2021/12/049412.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.100
GPT teacher head0.364
Teacher spread0.264 · 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 designRandomized trial
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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