Cost, time savings and effectiveness of wearable devices for remote monitoring of patient rehabilitation after total knee arthroplasty: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Total knee arthroplasty (TKA) is a surgical procedure primarily used to treat patients with end-stage knee osteoarthritis (KOA). Postoperative physical exercise is a critical part of the overall treatment of KOA and can bring significant benefits to the patients' recovery. Wearable devices can monitor patients' exercise data and upload it to the physician's workstation. This allows the rehabilitation physician to make timely adjustments based on the patients' movement feedback, and the surgeon can be informed of the patients' functional status. Overall, this study aims to evaluate the effectiveness of using wearable monitoring devices for rehabilitation exercise after TKA, with a focus on cost, time savings, and patient outcomes. METHOD/DESIGN: This is a single-center, single-blinded, parallel randomized controlled trial conducted at Xi'an Honghui Hospital, a regional orthopedic medical center. Eligible patients will be recruited to participate in the study, and baseline data collection and clinical assessments will be conducted at the time of admission. Using the principle of random allocation, recruited patients will be divided into either the experimental or control group. Both groups will undergo a standard, widely promoted rehabilitation program. The patients in the experimental group will wear equipment to detect and track mobility in the lower limbs. All patients will return to the outpatient clinic for follow-up assessments at 2 weeks, 12 weeks, and 24 weeks after discharge, where outcome indicators will be measured. The primary outcome will be the cost and time after discharge, while secondary outcomes will include the 6-min walk test (6MWT), range of motion (ROM), visual analog scale (VAS), American Knee Society Score (KSS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). DISCUSSION: We should encourage the adoption of novel, easy-to-use, supervised devices if they prove to be beneficial for patients in terms of cost, time, and effectiveness after TKA. This type of device is particularly important for people in remote rural areas, those with limited financial resources, and those who are reluctant to return to hospitals for follow-up care. Trial registration Chinese Clinical Trial Registry ChiCTR2300068418. Registered on 17 February 2023.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.007 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".