Reimagining Outcomes: A Perspective Review of Advances in Remote Monitoring Technologies in Post‐Arthroplasty Patient Care
Bibliographic record
Abstract
Arthroplasty surgery is a common and successful end-stage intervention for advanced osteoarthritis. Yet, postoperative outcomes vary significantly among patients, leading to a plethora of measures and associated measurement approaches to monitor patient outcomes. Traditional approaches rely heavily on patient-reported outcome measures (PROMs), which are widely used, but often lack sensitivity to detect function changes (e.g. gait limitations) that may persist after surgery. Accessible measurement systems for objectively capturing functional outcomes have steadily emerged recently. Notably, wearable motion sensing and sensor-embedded prostheses offer high-resolution, real-time data on patient mobility, revealing discrepancies between PROMs and functional recovery trajectories. Coupled with advancements in mobile health platforms, opportunities for remote monitoring and remotely engaging arthroplasty patients is burgeoning. Smartphone applications have improved adherence to rehabilitation protocols, pain management, and patient satisfaction while enabling remote care and reducing healthcare utilization. However, barriers such as inconsistent protocols, the need for clinical validation, reliance on patient compliance with sensor use, small sample sizes, privacy concerns, cost and reimbursement challenges, and limited long-term data remain. Other emerging technologies are further enabling uptake, including but not limited to smart implants, in-home monitoring systems, and artificial intelligence (AI)/machine learning (ML)-enhanced analyses. Together, these technologies hold promise for more personalized, cost-effective strategies for comprehensive and patient-centered assessments that can inform tailored rehabilitation approaches, allow for near real-time assessment of patient outcomes, improve function, and promote earlier mobilization. Further research should focus on standardization and clinical validation, economic and environmental impact, and long-term efficacy to optimize their integration into routine clinical practice.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".