HEALTH-RELATED QUALITY OF LIFE (HRQOL) IN OLDER ADULT SUBJECTS WITH KNEE OSTEOARTHRITIS PAIN
Bibliographic record
Abstract
Aim of the study: The study aims to find the health-related quality of life (HRQoL) in older adult subjects with knee osteoarthritis pain. Material and methods: A cross-sectional, telephonic study was done on knee osteoarthritis subjects above 50 years of age. Socio-demographic scales and details,(ShalliBavoria, et al., 2020) i.e., WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index), SF-12 (Short Form survey), PHQ-9 (Patient Health Questionnaire) et al were applied( Soo-Hyun Park and Byeong-Hun Kang., 2020). Information was retrospectively compiled and a semi-structured proforma was selected to gather the clinical variables. Results: Results were identified by domains of Health related quality of life.Results showed that there is significant difference in general health related quality of life based on the KL grading of KOA.(P=0.02,P=0.04)Study concluded that general HRQoL worsens with higher grading of KOA.The results showed that there was a substantial discrepancy witnessed between males and females where females encountered more pain.As stiffness increases HRQoL decreases. More severity of pain was observed in depressed people.(Roger B. Fillingim, et al., 2020) Conclusion: Daily challenges and actions, of which some may be unusual to sufferers in a pastoral setting in India, underlie sedentary and effective strategies to Osteoarthritis and its control. The study concluded that a significant difference in pain and general quality of life-based on the KL grading of KOA (P=0.02, P=0.04) was observed.(Hye-Young Shim, etal, 2018) It was also found that there is a considerable difference in pain based on the duration of ]knee osteoarthritis (P=0.05).( Aliasghar A. Kiadalir, et al., 2017).
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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 | 0.001 | 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".