A Pilot Study on the Efficacy of an App-Based Rehabilitation Counselling Program after Total Knee Arthroplasty
Bibliographic record
Abstract
The aim of this study was to determine the effects of a novel app-based rehabilitation counselling program in patients recovering from total knee arthroplasty. In the app-based rehabilitation counselling program, a counselor provides one-on-one rehabilitation exercises and management-focused psychological counselling after total knee arthroplasty (TKA). This study included 42 patients, who were divided into three groups of 14 patients each: Group A, whose rehabilitation training was based on a guideline leaflet; Group B, whose rehabilitation was based on the app-based rehabilitation counselling program; and Group C, whose rehabilitation training was based on continuous passive motion combined with the app-based rehabilitation counselling program. To determine the effects of rehabilitation, the isokinetic knee muscle strength and knee joint range of motion were measured in addition to knee function tests such as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the visual analogue scale (VAS) for pain at two and three months after rehabilitation. The comparison of the means of the three groups was analyzed using one-way analysis of variance (ANOVA), with Group C showing significant variance in isokinetic knee muscle strength (p < 0.05), passive ROM (p < 0.01), and WOMAC (p < 0.05) after three months. As a result, this study confirmed the positive potential of the app-based rehabilitation counselling program.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".