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Physical function evaluation in older patients with knee osteoarthritis

2023· article· en· W4391481142 on OpenAlexaboutno aff
Vu Thi Diu, Nguyễn Thị Hoài Thu, Nguyen Ha Giang, Tran Viet Luc, Nguyen Trung Anh

Bibliographic record

VenueTạp chí Nghiên cứu Y học · 2023
Typearticle
Languageen
FieldHealth Professions
TopicProblem Solving Skills Development
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisPhysical medicine and rehabilitationMedicinePhysical therapyFunction (biology)Alternative medicinePathology

Abstract

fetched live from OpenAlex

Knee osteoarthritis is one of the most common joint disorders of older individuals. It is a painful and disabling disease characterized by pain, stiffness. However, in Vietnam, the number of studies on this issue is still very limited, especially those on physical function. Objective: This study aimed to evaluate physical function in 178 elderly patients with knee OA. A cross-sectional study was utilized. Physical function was assessed by The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Timed up and go test (TUG). Mean total WOMAC scores of older patients with knee OA in the study was: 34.67 (± 15.63). The proportion of older patients with knee OA with impaired mobility (assessed by TUG test) was 64.8%. The mean total WOMAC scores were more likely to be higher in the elderly with knee OA whohave advanced age, living with others (family, caregiver), overweight/obese or underweight, poor nutritional status, no sleep disturbance, more pain, inflammatory pain and had no treatment for knee OA. There was a decrease in physical function in patients with osteoarthritis of the knee. Mobility impairment was associated with the level of pain, and treatment of knee OA or lack of.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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)0.0010.000

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 head0.027
GPT teacher head0.350
Teacher spread0.323 · 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 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
Published2023
Admission routes1
Has abstractyes

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