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Record W2793229076 · doi:10.1055/s-0038-1637018

The Role of Virtual Rehabilitation in Total and Unicompartmental Knee Arthroplasty

2018· article· en· W2793229076 on OpenAlexaboutno aff
Morad Chughtai, John J. Kelly, Jared M. Newman, Assem A. Sultan, Anton Khlopas, Nipun Sodhi, Anil Bhave, Michael C. Kolczun, Michael Mont

Bibliographic record

VenueThe Journal of Knee Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTelerehabilitationWOMACPhysical therapyUnicompartmental knee arthroplastyUsabilityRehabilitationArthroplastyPatient satisfactionOsteoarthritisTelemedicineSurgeryHealth care

Abstract

fetched live from OpenAlex

Abstract This study evaluated the use of telerehabilitation during the postoperative period for patients who underwent total knee arthroplasty (TKA) or unicompartmental knee arthroplasty (UKA). Specifically, this study evaluated the following: (1) patient compliance and adherence to the program, (2) time spent performing physical therapy exercises, (3) the usability of the virtual rehabilitation platform, and (4) clinical outcome scores in a selected primary knee arthroplasty cohort. A total of 157 consecutive patients underwent TKA (n = 18) or UKA (n = 139). These patients used a telerehabilitation system with an instructional avatar, three-dimensional motion measurement and analysis software, and real-time televisit capability designed for arthroplasty patients. Compliance was determined by how many times the patients followed prescribed repetitions of exercises. The total time spent performing exercises for each patient was collected. The usability of the virtual rehabilitation platform (on the patient's end) was evaluated using the system usability scale (SUS) questionnaire. The number of in-person and televisits was recorded for each patient. Patient-reported outcomes were collected through the patient and clinician interfaces and included the Knee Society Score (KSS) for pain and functions, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, and Boston University Activity Measure for Post-Acute Care (AM-PAC) score. Patients spent an average of 29.5 days partaking in the therapy. TKA and UKA patients had a mean of 3.5 and 3.2 outpatient follow-up visits, each, for in-office therapy with a physical therapist, respectively. This figure exceeded the mean number of real-time virtual patient–clinician visits by 0.8 visits per patient in the TKA cohort and by 1 visit per patient in the UKA cohort. Patients spent on average 26.5 minutes per day conducting an average of 13.5 exercises. By the end of rehabilitation, patients had spent an average of 10.8 hours performing exercises, and of all the exercises performed, approximately 21 exercises were uniquely designed. Mean SUS score in the cohort was 93 points, which was interpreted as being above the 50th percentile point of the scale. Following therapy, KSS pain and function scores improved markedly and the improvements were measured at 368% for TKA and 350% for UKA (pain) and 27% for UKA and 33% for TKA (function). In addition, WOMAC scores improved by 57% and 66% for UKA and TKA patients while the improvement in AM-PAC scores was at 22% and 24%. This telerehabilitation platform encouraged clinician–patient interaction beyond the hospital setting and offers the advantage of cost savings, convenience, at-home monitoring, and coordination of care, all of which are geared to improve adherence and overall patient satisfaction. Additionally, the biometric data can be used to develop custom physical therapy regimens to assure proper rehabilitation, which is lacking in other telerehabilitation applications that use noninteractive videos that can be watched on mobile devices and tablets.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.242
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations75
Published2018
Admission routes1
Has abstractyes

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