Reproducibility and validity of the DynaPort KneeTest
Bibliographic record
Abstract
OBJECTIVE: To determine the reproducibility and validity of the DynaPort KneeTest, a performance-based test that measures quality of movement of patients undergoing total knee replacement (TKR). METHODS: A total of 92 patients with osteoarthritis (OA) of the knee performed the KneeTest twice on the same day; 94 healthy controls performed the KneeTest once. During the test, 29 activities were performed with accelerometers attached to the body. Relevant functional parameters were extracted from the accelerometers. A selection of parameters was used to calculate activity scores, based on the ability of parameters to discriminate between patients and controls (regression analyses). Based on internal consistency analyses (Cronbach's alpha), redundant activities were removed. Four cluster scores and a total KneeScore were calculated from the remaining activity scores. Reproducibility and validity of the cluster scores and the total KneeScore 2 were assessed. RESULTS: Based on internal consistency analyses, the test was reduced to 23 activities. Inter- and intraobserver reliability using intraclass correlation coefficients were 0.90 (0.83-0.94) and 0.95 (0.83-0.98), respectively. Limits of inter- and intraobserver agreement were -8.3 to 11.3 and -4.2 to 9.0. Construct validity was confirmed by expected correlations with the Western Ontario and McMaster University Osteoarthritis Index physical functioning (0.55), Medical Outcomes Study Short Form-36 Health Survey physical functioning (0.62), and Knee Society Score function (0.64). CONCLUSION: The KneeTest is a useful performance-based measure for research in patients with knee OA undergoing TKR, with good reliability and validity. Further research is required to improve its usefulness for 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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".