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Record W4409711278 · doi:10.63520/pprj.v2i1.310

STRETCHING HAMSTRING EXERCISE DAN STRENGTHENING QUADRICEP MUSCLE EXERCISE TERHADAP PENINGKATAN AKTIVITAS FUNGSIONAL PENDERITA OSTEOARTHTRITIS GENU

2023· article· en· W4409711278 on OpenAlexaboutno aff
Anisa Dwi Yuliyani

Bibliographic record

VenuePhysiotherapy and Physical Rehabilitation Journal · 2023
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHamstringPhysical therapy

Abstract

fetched live from OpenAlex

In the population of developing countries, moderate or severe disability due to osteoarthritis is 10 percent, while in the population of low-income countries it reaches 33.5%, and disability due to arthrosis occupies 43.4% of the world's population. Osteoarthritis affects about 21% of adults (46.4 million people) in the United States, and this number is expected to increase to 67 million by 2030. Hamstring stretching exercises are very effective at increasing the flexibility of muscles and joints, thereby reducing or eliminating joint pain. This exercise can also improve circulation and strengthen bones. Strengthening the quadriceps muscles helps reduce pain, improves body function and quality of life for patients and slows disease progression. WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) is an index used to assess the condition of patients with osteoarthritis of the knee. Questions consisted of pain, stiffness, physical and social functioning. This study used a pre-experimental method with a pre-test and post-test research design that aimed to determine the effect of giving stretching hamstring exercise and strengthening quadriceps muscle exercise. The conclusion is that there is an effect of stretching hamstring exercise and strengthening quadriceps muscle exercise on increasing the functional activity of patients with osteoarthritis genu

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), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
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
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.426
Teacher spread0.390 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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