Influence of pain severity on health-related quality of life in Chinese knee osteoarthritis patients.
Bibliographic record
Abstract
OBJECTIVE: The aim of this cross-sectional study was to examine the relationship among pain and other symptoms intensity, and health-related quality of life (HRQoL) in Chinese patients with knee osteoarthritis (OA). METHODS: The study was cross-sectional, descriptive, and correlational. A convenience sample of 466 patients with knee OA was recruited in the study. Age, gender, body mass index (BMI), duration of disease, and Kellgren- Lawrence (KL) scores were recorded. HRQoL and symptoms were assessed using the 36-item Short Form Health Survey (SF-36) and the Western Ontario and McMaster (WOMAC) index in participants. RESULTS: The sample was predominantly female (82%) with mean age 56.56 years and mean BMI 24.53 kg/m(2). We found that WOMAC subscale scores significantly negative correlated with the majority of SF-36 subscale scores in knee OA patients (P < 0.05). There were no correlations between BMI, duration of disease, KL score and the vast majority of SF-36 subscale scores in patients (P > 0.05). In addition, there was a significant correlation between age and PCS, gender and MCS in patients (P < 0.05). Regression analysis showed, WOMAC subscale scores significantly negative correlated with the vast majority of SF-36 subscale scores. WOMAC-pain score had the strongest relationship with SF-36 PCS and MCS scores. CONCLUSIONS: In summary, pain severity has a greater impact on HRQoL than patient characteristics, other joint symptoms and radiographic severity in Chinese knee OA patients. Relieving of knee symptoms may help to improve patients' HRQOL. The study provided the evidence that relieving pain should be the first choice of therapy for knee 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".