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Record W2809686517 · doi:10.3390/healthcare6030074

Functionality in Middle-Aged and Older Overweight and Obese Individuals with Knee Osteoarthritis

2018· article· en· W2809686517 on OpenAlexaboutno aff
Neda Akhavan, Lauren Ormsbee, Sarah Johnson, Kelli S. George, Elizabeth Foley, Marcus L. Elam, Zahra Ezzat‐Zadeh, Lynn B. Panton, Bahram H. Arjmandi

Bibliographic record

VenueHealthcare · 2018
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACOsteoarthritisMedicineOverweightBody mass indexPhysical therapyKnee painPopulationObesityInternal medicine

Abstract

fetched live from OpenAlex

Patients with knee osteoarthritis (OA) suffer from immobility and pain. The objective of this cross-sectional study was to investigate the relationship between pain and functionality in middle-aged and older overweight and obese individuals with mild-to-moderate knee OA. Overall pattern, physical activity, and total energy expenditure (TEE) were assessed in 83 participants. The Western Ontario McMaster Universities Arthritis Index (WOMAC) was used to assess lower extremity pain and function. The six-minute walk test (6-MWT) and range of motion (ROM) were also assessed. Results indicated that age was inversely associated with body mass index (BMI) (r = 0.349) and total WOMAC scores (r = 0.247). BMI was positively associated with TEE (r = 0.430) and WOMAC scores (r = 0.268), while ROM was positively associated with the 6-MWT (r = 0.561) and negatively associated with WOMAC (r = 0.338) and pain scores (r = 0.222). Furthermore, women had significantly greater WOMAC scores (p = 0.046) than men. Older participants (≥65 years old) had significantly lower BMI (p = 0.002), and distance traveled during the 6-MWT (p = 0.013). Our findings indicate that older individuals in this population with knee OA had lower BMI, greater ROM, and less pain and stiffness and walked slower than middle-aged individuals. Women reported greater pain, stiffness, and reduced functionality, indicating that the manifestation of OA may vary due to gender.

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)0.000
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 score0.079
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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