Application of robotic technology in biomechanics to study joint laxity
Why this work is in the frame
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Bibliographic record
Abstract
PRIMARY OBJECTIVE: To evaluate whether in vitro joint testing using a robot with six degrees of freedom is useful for evaluating changes in joint laxity as a result of chronic osteoarthritis (OA). RESEARCH DESIGN: Repeated measures. METHODS: Broyden's method of solving nonlinear systems of equations drove a hybrid method of load and position robotic control. Sheep stifles (knee joints) were loaded between 3 Nm of internal load through to 3 Nm of external load in 1 Nm increments. Kinematic and morphologic data from five healthy ovine stifles were compared to the chronic OA effects in four surgically destabilized stifles. RESULTS: Stifles with chronic OA showed increases in stiffness while range of motion decreased. Gross morphologic changes included osteophytes and cartilage fibrillation. DISCUSSION: Robotic testing proved useful for evaluating changes in joint mechanics as a result of chronic OA. We observed morphological changes and associated increases in joint stiffness and decreased laxity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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