Effect of a Self-Care Application on Pain and Motor Rehabilitation Following Total Knee Arthroplasty
Bibliographic record
Abstract
Background and purpose: Today, development of telemedicine technology has led to wide use of smartphone to connect patients and health care teams to improve patient care. The aim of this study was to determine the effect of a self-care application on pain and mobility rehabilitation in patients following total knee arthroplasty surgery. Materials and methods: A randomized controlled clinical trial was carried out in 100 patients who were candidates for knee arthroplasty surgery at Tehran Baqiyatullah (Aj) Hospital. In this study, the experimental group was provided with a self-care application and the control group received routine hospital care. At days 7 and 14 after the surgery, the two groups were evaluated for pain and mobility rehabilitation using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Von Korff Pain Intensity and Disability Score. Results: Out of 100 people, 30 were men and 70 were women with an average age of 48.66±15.62. Findings showed significant differences between the two groups, at day 14 after the surgery, in mobility rehabilitation (P= 0.004) and pain (P= 0.001) at 95% confidence interval. Conclusion: According to this study, the self-care application improved pain and motor recovery after total knee arthroplasty surgery. (Clinical Trials Registry Number: IRCT20210724051973N1)
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".