New Accelerometric Method to Discriminate Between Asymptomatic Subjects and Patients With Medial Knee Osteoarthritis During 3-D Gait
Bibliographic record
Abstract
This study presents a new method to estimate 3-D linear accelerations at tibial and femoral functional coordinate systems. The method combines the use of 3-D accelerometers, 3-D gyroscopes and reflective markers rigidly fixed on an exoskeleton and, a functional postural calibration method. Marker positions were tracked by a six-camera optoelectronic system (VICON 460, Oxford Metrics). The purpose of this study was to determine if this method could discriminate between medial osteoarthritic and asymptomatic knees during gait. Nine patients with osteoarthritic knees and nine asymptomatic control subjects were included in this study. Eighteen parameters representing maximal, minimal, and range of acceleration values were extracted during the loading and preswing to mid-swing phase periods, and were compared in both groups. Results show good discriminative capacity of the new method. Eight parameters were significantly different between both groups. The proposed method has the potential to be used in comprehending and monitoring gait strategy in patients with osteoarthritic 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".