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Record W4400302329 · doi:10.21275/sr24626155424

Concoction Effect of Advance Physiotherapy Intervention on Clinical Parameters in Osteoarthritis Knee Patients - A Case Report

2024· article· en· W4400302329 on OpenAlexaboutno aff
Bhalchandra S Kharsade, Pooja Chauresia

Bibliographic record

VenueInternational Journal of Science and Research (IJSR) · 2024
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisPhysical therapyIntervention (counseling)Physical medicine and rehabilitationAlternative medicinePathology

Abstract

fetched live from OpenAlex

Osteoarthritis is most common degenerative conditions, affecting 65 years of age and older. The main goal of osteoarthritis treatment in physiotherapy is to reduce pain, improve physical function, prevent disability, and enhance quality of life. Considering advances in Physiotherapy for osteoarthritis knee patients, this study aims to evaluate the concoction effect of advanced physiotherapy intervention as a cumulative approach on clinical parameters of osteoarthritis. Various parameters were assessed using different scales including the Numerical pain rating scale for pain, knee injury, and osteoarthritis outcome score and Western Ontario and McMaster University arthritis index for disability , while the functioning of the lower limb was assessed using the lower extremity functional index. Pre-treatment and post-treatment scores were evaluated with follow-up for six weeks. The present research concludes effect of advanced physiotherapy intervention has a significant effect on clinical parameters It has shown improvement in the strength of lower limb muscles with improved physical functioning and reduction in pain and disability caused by OA knee secondary to concoction advance physiotherapy treatment which directly improves the patient's quality of life in OA knee and provides a holistic approach for improvement towards condition.

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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.089
GPT teacher head0.583
Teacher spread0.494 · 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 designOther design
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
Published2024
Admission routes1
Has abstractyes

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