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Record W4402336748 · doi:10.21103/article14(3)_oa18

Association between Depression and Postural Balance in Older Adults with Knee Osteoarthritis in Saudi Arabia: A Cross-Sectional Study

2024· article· en· W4402336748 on OpenAlexaboutno aff

Bibliographic record

VenueInternational Journal of Biomedicine · 2024
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyDepression (economics)Balance (ability)OsteoarthritisMedicineAssociation (psychology)Physical medicine and rehabilitationPhysical therapyPsychologyAlternative medicinePathology

Abstract

fetched live from OpenAlex

Background: Several studies examined the reciprocal relationship between knee osteoarthritis (KOA), depression, and postural balance separately; however, few studies have investigated the relationship between them.Therefore, this study aimed to identify the relationship between postural balance and depression among patients with KOA in Saudi Arabia.Methods and Results: A total of 71 patients with KOA were recruited (36 males, 35 females) with a mean age of 55.08.6 years, mean knee pain of 7.21.0,and mean Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) of 44.911.6.Depression was measured using the Hospital Anxiety and Depression Scale (HADS).The participants were asked to complete the HADS, rate their pain in both knees using the Visual Analogue Scale (VAS) and the WOMAC, and undergo the Berg Balance Scale (BBS) to assess the postural balance.A significantly strong negative correlation was observed between postural balance and anxiety and depression in all the participants (BBS and HADS: r=-0.920,P<0.001), as well as a significant negative correlation between knee pain and postural balance (VAS and BBS: r=-0.26,P=0.029) Conclusion: Decrease in postural balance was associated with a higher level of anxiety and depression in addition to knee pain among patients with KOA.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.010
GPT teacher head0.319
Teacher spread0.309 · 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 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
Published2024
Admission routes1
Has abstractyes

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