Pain and Quality of Life in Osteoarthritis : Relationship With Demographic and Clinical Variables
Bibliographic record
Abstract
Background and aim: Osteoarthritis is a major cause of musculoskeletal pain and physical disability. It might have negative impact on quality of life. The aim of this cross-sectional study was to investigate the relationship of pain and quality of life with various demographic and clinical variables in osteoarthritis.Methods: 156 patients (mean age 56u00b110 years, 79.5% female, mean disease duration 7.2u00b16.4 years) with knee, hip, foot and/or hand osteoarthritis referring to the outpatient clinic of physical medicine and rehabilitation department of a university hospital were assessed. Assessment scales included severity of pain by 0-10 numeric rating scale, Osteoarthritis Quality of Life Scale (OAQoL), Health Assessment Questionnaire (HAQ), Western Ontario and McMaster Universities Index of Osteoarthritis (WOMAC) and the Nottingham Health Profile (NHP). Results: Pain was more severe amongst females (p<0.001). Moderate significant correlations (Spearman r: 0.50-0.70) were found between pain and WOMAC_Function, HAQ, WOMAC_Stiffness, NHP_Physical Mobility and NHP_Energy. Pain was not related with the number of joints affected by osteoarthritis. Linear regression model was performed to determine the factors which together explain the variability in quality of life (OAQoL). Potential factors, found to be statistically significant in univariate linear regression analyses were used in stepwise regression procedure to select the final multivariable model. Final model included Pain (NRS), HAQ, NHP_Energy and NHP_Social Isolation with an adjusted R2 of 0.645. Conclusions: Pain was related with physical function, stiffness and fatigue. Regression model including pain, physical function, fatigue and social isolation explained most of the variance in quality of life in osteoarthritis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".