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Record W4394603825 · doi:10.3233/ppr-240847

Efficiency of a structured squat-based program in knee osteoarthritis rehabilitation

2024· article· en· W4394603825 on OpenAlexaboutno aff
Hazal Genç, Esra Atılgan

Bibliographic record

VenuePhysiotherapy Practice and Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSquatRehabilitationPhysical therapyOsteoarthritisPhysical medicine and rehabilitationAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Squat exercise, in which eccentric and concentric contractions are present simultaneously in different muscle groups, is one of the closed kinetic chain exercises commonly used in knee rehabilitation. OBJECTIVE: In the scope of the study, our objective is to determine the effectiveness of a structured squat-based exercise approach in patients with knee osteoarthritis. METHODS: In this study, 75 patients diagnosed with knee osteoarthritis were randomly assigned to three distinct groups: isoinertial exercise, a combination of home exercise and electrotherapy, and exercise solely. The assessment encompassed the application of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the Time Up and Go test, evaluation of quadriceps and hamstring muscle strength and activation levels, along with pain threshold assessment. The treatment program was administered three days a week over a span of eight weeks and was assessed both prior to and following the intervention. RESULTS: Across all groups, considerable enhancements were noted in the majority of parameters. Particularly noteworthy were the substantial improvements observed in the first group, specifically concerning WOMAC total score (p = 0.001), muscle strength, activation levels (p = 0.001), and pain tolerance (p = 0.05). CONCLUSIONS: In the group in which we applied isoinertial exercise, a positive increase was observed in most of the parameters.We suggest that isoinertial exercise applications, which are generally encountered in sports fields, should be used in different fields in future studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.028
GPT teacher head0.445
Teacher spread0.417 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized 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

Citations1
Published2024
Admission routes1
Has abstractyes

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