Kinematic gait analysis of workers exposed to knee straining postures by Bayes decision rule
Why this work is in the frame
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Bibliographic record
Abstract
Deep knee flexion postures such as kneeling and squatting have been demonstrated, in recent review of occupational kneedisorders, as a risk factor of developing knee osteoarthritis (OA). This study investigates a probabilistic method to analyze kneegait kinematics measurements of workers exposed to knee straining postures to determine if they are in any way similar tothose of knee OA patients. The measurements we use are clinically relevant kinematic signals, namely the variation duringa locomotion gait cycle of the angles the knee makes with respect to the three-dimensional (3D) planes of flexion/extension,internal/external rotation, and abduction/adduction. Three groups of participants were used: a set of 24 workers exposed to kneestraining postures (KS workers) acting as a test group, a control group of 25 non-KS posture workers, and a reference knee OAgroup of 29 subjects. We compared the kinematic data of KS workers to those of knee OA patients and non-KS subjects using theBayes decision theory. The results shows that, using the 3D data taken together or the abduction/adduction data, the KS workersresembles often to the OA patients. The analysis on the transverse plane and on sagittal plane, i.e., the flexion/extension and theinternal/external rotation, are not conclusive as the similarities are not significant. The kinematic gait analysis by Bayes decisionrule shows the similarity of workers exposed to knee straining postures to OA gait pattern and justifies further prospective studiesof KS workers in order to assess if gait pattern could be modified even before the onset of the disease.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it