The Impact of Osteoarthritis on the Quality of Life of the Patient in the Kingdom of Bahrain
Bibliographic record
Abstract
Osteoarthritis (OA) is a leading cause of disability and a decline in health-related quality of life (HRQoL). Healthcare professionals should prioritize the optimal measurement of HRQoL in patients with OA. To examine and assess the influence of Osteoarthritis on patients' quality of life in the Kingdom of Bahrain. In this cross-sectional study, a cohort of 149 individuals diagnosed with Osteoarthritis was included. Data related to QoL was collected by using Mini-Osteoarthritis Knee and Hip Quality of Life (Mini-OAKHQOL) and Western Ontario &McMaster Osteoarthritis Index (WOMAC). The Spearman rank correlation test was used to assess the correlation between assessments on different instruments used in osteoarthritis patients. The study involved participants with a mean age of 56.7 ± 11.7 years. Most of the patients experienced extreme pain during prostrating in prayer time (45.6%), sitting (32.2%), and while doing heavy domestic duties (30.9%). The Cronbach alpha coefficients for Mini-OAKHQOL and WOMAC ranged from 0.83 to 0.89 and 0.85 to 0.96 respectively. Only a few demographic factors significantly and positively correlated with Mini-OAKHQoL. This study concludes that OA has a substantial impact on HRQoL. This study can benefit healthcare professionals and policymakers to develop specific interventions and public health initiatives to address the multifaceted nature of osteoarthritis and improve the lives of those affected.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".