Gesture as an important factor in 3D kinematic assessment of the knee
Bibliographic record
Abstract
Contradictions exist between studies of the 3D kinematics of the knee. We hypothesize that they are in part due to differences in the gesture performed by the subjects during kinematic assessment. The purpose of this study is to evaluate the impact of gesture variations on knee kinematics. Seventeen healthy male subjects performed 20-s series of knee-bends in a knee-bend standardizing structure. All series differed regarding either foot rotation, knee excursion, or hip rotation. 3D knee kinematics were recorded using optical position sensors mounted on a skin-motion-reducing harness. Kinematic comparisons were made between a gesture of reference (the standard gesture) and every other gesture. Analyses were performed on average differences. Differences of up to 15 degrees of tibial rotation were found for gestures involving different foot rotation. Gestures involving different knee excursion brought on differences of more than 4 degrees of tibial rotation while hip rotation induced more than 5 degrees of tibial rotation. It is hereby demonstrated that gesture differences can have a dramatic impact on measured knee kinematics. Hence gesture performance needs to be carefully monitored during 3D kinematic assessment of the weight-bearing human knee.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".