The Effect of Video-Assisted Education on Knee Function and Quality of Life after Total Knee Replacement: A Randomized Controlled Trial
Bibliographic record
Abstract
Objective: This study aims to investigate the effect of video-assisted patient education (VPE) on knee functions, quality of life (QoL) and decrease complications in total knee replacement (TKR) patients. Methods: This study is designed as a prospective, parallel, two-arm, randomized clinical trial. It included 44 patients who underwent elective TKR at an orthopedics and traumatology clinic. The VPE group received VPE including early postoperative care for TKR, activities of daily living, and gradual exercise program in addition to the existing routine care at the clinic. The control group received only routine care. The results were collected with Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Short Form-36 (SF-36) scores measured at baseline, 1st and 3rd months after TKR. Results: The mean scores of the VPE group on the pain, stiffness, and physical function of the WOMAC were significantly lower in 3rd month after TKR than the control group. The VPE group had significantly higher scores than the control group on SF-36 of all subcategories except pain, emotional role, and mental health in 3rd month after TKR. Conclusion: The VPE can improve knee function and QoL in TKR patients. Nurses can use the VPE method in patients to improve knee functions and QoL after TKR.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".