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Record W3023565668 · doi:10.1177/2210491720921183

Factors associated with health-related quality of life in Japanese patients with hip osteoarthritis: A cross-sectional study

2020· article· en· W3023565668 on OpenAlexaff
Shigeharu Tanaka, Shawn M. Robbins, Yu Inoue, Ryo Tanaka

Bibliographic record

VenueJournal of Orthopaedics Trauma and Rehabilitation · 2020
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsMcGill UniversityCentre de réadaptation Lethbridge-Layton-MackayCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsMedicinePhysical therapyOsteoarthritisQuality of life (healthcare)Cross-sectional studyRange of motionRehabilitationHealth related quality of lifeAffect (linguistics)DiseaseInternal medicineAlternative medicinePsychology

Abstract

fetched live from OpenAlex

Background/Purpose: Chronic symptoms related with hip osteoarthritis (OA) can negatively affect health-related quality of life (HRQoL). The purpose of this study was to examine factors related to a HRQoL measure that considers an Asian lifestyle in Japanese patients with hip OA. Methods: Forty-seven female subjects participated. The dependent variable was the Japanese Orthopaedic Association Hip Disease Evaluation Questionnaire (JHEQ), which assessed HRQoL. Potential factors were measured as independent variables. After screening, potential variables were entered into a multiple regression analysis to determine which variables were related to HRQoL. Results: In the regression model, knee extension muscle strength on the unaffected side and hip flexion range of motion (ROM) on the affected side were associated with HRQoL. Higher strength and higher ROM were related to greater HRQoL. Conclusion: Results can help health-care providers develop appropriate rehabilitation programs for improving HRQoL in patients with hip 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 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)0.001
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 score0.076
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.041
GPT teacher head0.298
Teacher spread0.258 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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