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Record W4387668958 · doi:10.1186/s43166-023-00220-x

The effect of combined balance and strength exercise program in patients with different grades of primary knee osteoarthritis

2023· article· en· W4387668958 on OpenAlexaboutno aff
Yousra Hisham Abdel‐Fattah, Mowaffak Moustafa Abdel Hamid, Sara Ibraheem Mohamed Seleem Metawaa, Sarah Sayed El-Tawab

Bibliographic record

VenueEgyptian Rheumatology and Rehabilitation · 2023
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersAlexandria University
KeywordsOsteoarthritisWOMACBalance (ability)MedicinePhysical therapyDynamic balancePhysical medicine and rehabilitationRehabilitationKnee JointSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

Abstract Background Knee osteoarthritis (KOA) is a degenerative disease that affects all parts of the joint including the surrounding ligaments, tendons, and muscles. Biomechanical changes that occur in KOA cause aggravation of symptoms with further joint damage. Thus, modifying the biomechanics of the knee joint may help in the prevention and treatment of KOA. For that reason, our aim was to assess the effect of combined balance and strengthening exercise programs in patients with different grades of primary KOA. Results All studied groups showed comparable significant improvement in quadricep muscle strength, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score (< 0.001), time-up and go score (< 0.001), 6-m walk time (< 0.001), and dynamic balance (< 0.001) at the end of exercise program. Furthermore, patients with mild-moderate KOA showed a significant improvement in pain, physical function, total WOMAC scores, and dynamic balance compared to those with more severe KOA. Conclusion Combined balance and strengthening exercise programs may help improve pain, physical function, and dynamic balance in patients with KOA regardless of its severity. However, following exercise patients with milder forms of KOA may show greater improvement compared to patients with severe KOA.

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 score0.224
Threshold uncertainty score0.270

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 head0.002
GPT teacher head0.209
Teacher spread0.206 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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