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Record W2892353359 · doi:10.3233/bmr-181234

Comparison of effectiveness of the home exercise program and the home exercise program taught by physiotherapist in knee osteoarthritis

2018· article· en· W2892353359 on OpenAlexaboutno aff
Merve Ebrar YILMAZ, Mustafa ŞAHİN, Candan Algün

Bibliographic record

VenueJournal of Back and Musculoskeletal Rehabilitation · 2018
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisPhysical therapyMedicinePhysical medicine and rehabilitationAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Home-based exercise therapy is effective in reducing pain and improving function in adults with osteoarthritis of the knee. OBJECTIVE: To investigate and compare the effectiveness of the home exercise program and the home exercise program taught by a physiotherapist in knee osteoarthritis. METHODS: The study was conducted with 80 patients with knee osteoarthritis. The patients were randomized into two groups. The first group was given the home exercise brochure by the orthopedist, while the second group did home exercises under the guidance of the physiotherapist. The goniometer for the range of motion (ROM) of the knee, Myometer for evaluation of the quadriceps and hamstring muscles strength, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Short Form Health Survey (SF-36) were used for evaluation. RESULTS: Statistically significant improvements were found in the post-treatment ROM, VAS, quadriceps and hamstring muscles strength, WOMAC and SF-36 values in both groups (p< 0.05). When the change values were compared, the evaluation results of group II were better than group I statistically (p< 0.05). CONCLUSIONS: This study proved that home exercises taught by a physiotherapist were more useful for patients with knee osteoarthritis. When the home exercise program is implemented, a new role is created for a physiotherapist.

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 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 score0.929
Threshold uncertainty score0.410

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 head0.005
GPT teacher head0.297
Teacher spread0.292 · 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

Citations27
Published2018
Admission routes1
Has abstractyes

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