[Impact of the COVID-19 Pandemic on Health-Related Quality of Life in Older Adults With Preoperative Knee Osteoarthritis].
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis is prevalent in older adults worldwide. Quality of life was negatively affected by the COVID-19 pandemic. PURPOSE: This study was designed to examine osteoarthritis severity and health-related quality of life (QOL) in older adults with knee osteoarthritis before total knee replacement during the COVID-19 pandemic and to identify the related predictors of QOL. METHODS: This cross-sectional correlation study involved convenience sampling in the orthopedic ward of a regional teaching hospital in central Taiwan from June 2020 to June 2021 using the Western Ontario and McMaster Universities Arthritis Index and the SF-36v2 Health Survey. The data were analyzed using Pearson's correlation coefficient analysis, independent samples t test, and one-way analysis of variance to determine correlations among demographic variables, osteoarthritis severity, and QOL. The predictors of QOL were examined using stepwise multiple linear regression analysis. RESULTS: A total of 60 older adults diagnosed with knee osteoarthritis were sampled. The average age was 70 years and the average osteoarthritis severity score was 70.45. Being male, having comorbidities, and having a relatively high level of monthly disposable income were associated with poorer QOL. Moreover, more severe knee pain, stiffness, and physical dysfunction were associated with better psychological QOL. CONCLUSIONS / IMPLICATIONS FOR PRACTICE: During the COVID-19 pandemic, the severity of knee osteoarthritis affects preoperative quality of life in older adults. Clinicians should detect signs of pain and physical dysfunction in these patients in advance and intervene in a timely manner to improve their QOL before surgery.
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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 | 0.003 |
| 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 | 0.001 |
| 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".