Conditions of Use, Reliability, and Quality of Audio/Video-Mediated Communications During In-Home Rehabilitation Teletreatment for Postknee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Audio/video-mediated communication between patients and clinicians using videoconferencing over telecommunication networks is a key component of providing teletreatments in rehabilitation. OBJECTIVE: The objectives of this study were to (1) document the conditions of use, performance, and reliability of videoconferencing-based communication in the context of in-home teletreatment (TELE) following total knee arthroplasty (TKA) and (2) assess from the perspective of the providers, the quality attributes of the technology used and its impact on clinical objectives. MATERIALS AND METHODS: Descriptive embedded study in a randomized controlled trial using a sample of 97 post-TKA patients, who received a total of 1,431 TELE sessions. Technical support use, service delivery reliability, performance, and use of network connection were assessed using self-report data from a costing grid and automated logs captured from videoconferencing systems. Physical therapists assessed the quality and impact of video-mediated communications after each TELE session on seven attributes. RESULTS: Installation of a new Internet connection was required in 75% of the participants and average technician's time to install test and uninstall technology (including travel time) was 308.4 min. The reliability of service delivery was 96.5% of planned sessions with 21% of TELE session requiring a reconnection during the session. Remote technical support was solicited in 43% of the sessions (interventions were less than 3-min duration). Perceived technological impacts on video-mediated communications were minimal with quality of the overall technical environment evaluated as good or acceptable in 96% of the sessions and clinical objectives reached almost completely or completely in 99% of the sessions. CONCLUSIONS: In-home rehabilitation teletreatments can be delivered reliably but requires access to technical support for the initial setup and maintenance. Optimization of the processes of reliably connecting patients to the Internet, getting the telerehabilitation platform in the patient's home, installing, configuring, and testing will be needed to generalize this approach of service delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".