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Record W4317212440 · doi:10.7759/cureus.33918

A Case Report on Core Muscles Training for Knee Osteoarthritis Through Core Muscles Activations and Gait Analysis

2023· article· en· W4317212440 on OpenAlexaboutno aff
D. Maryama Ag. Daud, Shye Nee Liau, Suhaini Sudi, Malehah Mohd Noh, Nyein Yin Khin

Bibliographic record

VenueCureus · 2023
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsnot available
FundersUniversiti Malaysia Sabah
KeywordsMedicineOsteoarthritisGaitWOMACPhysical therapyPhysical medicine and rehabilitationCore (optical fiber)Knee painKnee JointSurgeryPathology

Abstract

fetched live from OpenAlex

Knee osteoarthritis (OA) is a chronic joint disease that can affect all ages, but it is more common in the elderly. Pharmacological and non-pharmacological treatments have been invented evolutionarily over the years to halt this disease. Exercise is one of the first-line treatments for knee OA as well as for prevention. This case study features a 47-year-old man who has grade IV bilateral knee OA and has never had any surgery and takes fish oil daily as a supplement. His walking pattern was significantly impacted by the chronic knee discomfort he had in both legs. Thus, the walking gait of this patient was analyzed together with core muscle activation before and after two weeks of core resistance exercise intervention. The knee pain score was assessed using the Western Ontario and McMaster Universities Index (WOMAC). The outcomes of this research depict that core resistance training has the potential to be used as an alternative, non-surgical and non-pharmacological treatment for a patient with knee OA.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.003
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0040.002

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 head0.124
GPT teacher head0.304
Teacher spread0.180 · 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 designCase report
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

Citations5
Published2023
Admission routes1
Has abstractyes

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