Health-Related Quality of Life and Outcome after Total Knee Replacement: Results from a Cross-Sectional Survey in Jordan
Bibliographic record
Abstract
Total knee replacement (TKR) is a common procedure that is considered cost-effective with excellent long-term survivorship. This cross-sectional study, which includes 118 selected patients in Jordan from January 2020 to January 2021, aims to assess health-related quality of life (HQOL), functional status, and associated factors before and after 3 to 12 months of TKR. Furthermore, several demographical factors were included in this study to evaluate the potential association with the procedure's outcome. Western Ontario and McMaster Universities Osteoarthritis Index scale (WOMAC) and Short Form-36 (SF-36) scale were used in this study. It was found that the main change occurred 6-12 months after the procedure. Gender and age were found to be the primary predictor of quality of life among TKR patients. Patients before the TKR procedure experienced severe pain, joint stiffness, physical impairment, and psychological demand issues. After the TKR procedure, all symptoms were improved significantly. The TKR procedure can improve the quality of life for patients, which can be detected for several months after the procedure. Function and pain are the most critical indicators of improvement in quality of life. Assessing the quality of life is a key indicator for identifying patients without improvement, as well as improving the health care process and comparing other alternative interventions.
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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 | 0.001 |
| 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".