Interrater Agreement Between Telerehabilitation and Face-to-Face Clinical Outcome Measurements for Total Knee Arthroplasty
Bibliographic record
Abstract
Outcome measures in physical therapy provide the basis for determining the patient's rehabilitation needs, developing an individual intervention plan, and reassessing the evolution of the condition after therapeutic intervention. Questions surrounding the validity and reliability of outcome measures obtained in the context of telerehabilitation remain. The goal of this study was to explore which outcome measures can be used reliably in the context of telerehabilitation after discharge from an acute care hospital for lower limb orthopedic surgery. Fifteen patients recently discharged after total knee arthroplasty were evaluated by two experienced therapists. Each therapist evaluated under a given condition (face-to-face assessment, telerehabilitation assessment) eight outcome measures taken from standard clinical tests routinely used in the management of orthopedic rehabilitation after total knee arthroplasty. Evaluations were measured at 1-day intervals. Telerehabilitation evaluations were conducted with a videoconference link (H.264 CoDecs with Pan, Tilt, Zoom cameras) between either the participant's home or a clinical environment and a remote clinical station over residential DSL lines at 512 kbps. Interrater agreement between the two measurement modes was analyzed using the Bland and Altman method and Kripendorff's alpha reliability estimate. The 95% confidence interval for mean difference between evaluation methods varied between -20% and 8% for knee range of motion measures, -85% and 55% for scar management, -33% and 29% for functional evaluations. Five out of the eight outcome measures showed reliability estimates of >0.80, with lowest reliability obtained for the scar assessment scale (0.34) and the highest reliability for the evaluation of the range of motion at the knee (0.87 in flexion and 0.85 in extension). Clinical variables typically measured in face-to-face evaluations can be measured successfully under telerehabilitation conditions with moderate reliability.
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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 | 0.049 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".