The Effects of Pedometer-Based Intervention on Patients After Total Knee Replacement Surgeries
Bibliographic record
Abstract
Background: Non-compliance is considered a major concern that challenges health care providers after total knee replacement (TKR). Noncompliance may lead to increase pain, loss of muscle strength, increase swelling, loss of normal movement and functional limitations. Pedometer was found to increase physical activity compliance in many populations. However, pedometers effect on rehabilitation outcomes in patients after TKR was not examined yet. Objectives: The aim of this study was to examine the effects of pedometer based intervention on patients’ rehabilitation outcomes following TKR surgeries. Design: Randomized controlled trial Materials and methods: 20 TKR patients were randomized into: pedometer group (n=10) and control group (n=10). Both groups received the same rehabilitation program. However, pedometers were given to the pedometer group patients in day 1 after surgery for seven consecutive days. Outcome measurements included: knee range of motion (ROM) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Results: After seven days, knee flexion ROM and physical function scores were significantly increased and pain score and stiffness was significantly decreased in pedometer group compared with the control group. Conclusion: Pedometer is a wide spread, cheap, conservative and easily used device that could be used to increase compliance and improve knee outcomes in patients 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".