Alterations in the Relative Surface Velocity of Joint Following Anterior Cruciate Ligament Injury in a Sheep Model
Bibliographic record
Abstract
Osteoarthritis is characterized as a potentially disabling chronic disease, which can decrease the quality of life of individuals. To date, many aspects of osteoarthritis remain unclear but epidemiologic studies have shown that people with a history of joint injury (especially in the knee joint) are at a high risk of developing OA. Recently, it has been suggested that changes in a knee joint relative surface velocity correlate more consistently with cartilage damage after joint injury. The reason can be explained by the fact that the directions of surface shear forces are related to the direction of surface relative velocity. Thus, an objective of this study was to determine the relative surface velocity of the knee joint before and after ligament injury and to correlate the change in the relative velocity with cartilage damage in a sheep model. We derived analytical formulas to define the linear and angular velocity of the tibiofemoral component of a knee joint. The results are compatible with the motion of the joint during a normal gait cycle. The results also show that there were changes in the timing of the phases of the gait cycle between the intact and follow-up time points after injuries in the subjects, evidence of changes in the direction of the relative velocity. This study provides new information about the role of joint surface relative velocity of the knee joint after ligament injury, which can be considered as a potential mechanical factor for cartilage damage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".