Day-to-Day Reliability of Two Different Models to Quantify Knee Kinematics in Subjects With Knee Osteoarthritis and Healthy Controls
Bibliographic record
Abstract
Results from instrumented gait analysis vary between test situations. Subject characteristics and the biomechanical model can influence the total amount of variability. The purpose of this study was to quantify reliability of gait data in general, and with respect to the applied model, and investigated population group. Reliability was compared between a functional and a predictive gait model in subjects with knee osteoarthritis and healthy controls. Day-to-day consistency for sagittal plane variables was comparable between models and population groups. Transversal plane variables relative to joint excursion showed larger inconsistency for repeated measures, even for a more sophisticated biomechanical approach. In conclusion, the presented reliability data of sagittal plane kinematics should be used for a reasonable interpretation of results derived in clinical gait analysis. Variables of the transversal plane should not be used as long as sources of error are not sufficiently minimized.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".