Virtual performance measure in osteoarthritis: An innovative transformation of patient care
Bibliographic record
Abstract
Objectives: The purpose of this study was to develop and establish reliability and validity of a virtual performance measure (VPM) score that encompassed 10 videos in patients with osteoarthritis of the knee joint. Patients' experience and satisfaction were documented. Design: Forty videos were chosen for 10 functional tasks, with four videos showing increasing difficulty for each task. Patients were requested to choose the video that best reflected their own situation. Clinical and radiological findings and self-report and performance measures were completed. Results: Data of 100 patients, 70 (70%) females, mean age: 65 ± 9 were examined. The Cronbach's alpha coefficient that examined internal consistency of the VPM score was 0.92. The intraclass correlation value of 0.82 was obtained for test-retest reliability. Factor analysis showed three distinct domains. There was moderate correlations between the VPM score and the self-report and actual performance measures ranging from r = 0.46 to 0.66. The VPM summated score of 10 activities was able to differentiate between candidates and non-candidates for knee arthroplasty, with the area under the curve value of 0.90 indicating excellent predictive validity. The overall patient experience and satisfaction was positive with 67% of participants feeling that virtual care could have an impact on minimizing physical presence in the clinic or hospital. Conclusions: The VPM is a reliable and valid outcome measure in patients with osteoarthritis of the knee joint. This digital tool has the potential to transform osteoarthritis care by providing a valid remote measurement of real-life functional limitations and reduce the burden of time consuming in-person tests.
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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 | 0.004 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".