Correlation of damage score in PTOA with changes in stress on cartilage in an ovine model
Bibliographic record
Abstract
Objective: There is a high risk of developing osteoarthritis (OA) following traumatic injury to the knee. Severe ligament injuries can disrupt the integrity of the multicomponent knee at both biological and biomechanical levels. We hypothesize changes in cartilages stresses could lead to tissue damage and development of OA. Design: The in-vivo gait kinematics of the stifle (knee) joint of four adult female ovine subjects were recorded prior to and at ten-and-twenty weeks following partial ACL-MCL transection. The subjects were sacrificed and the experimental joint from each subject was mounted on a parallel robotic system programmed with the kinematic findings. Ten custom-built Fibre Bragg Grating optic sensors were arranged to measure contact stresses on the surface of the tibial plateau articular cartilage. These sensors provide the first accurate stress measurements in a joint during gait replication using the previously recorded in-vivo kinematics. The relationship between the results obtained and observed focal damage was assessed. Results: link was detected between individual animal differences in kinematics and variations in stress magnitudes or the development of focal cartilage damage. Conclusions: The findings highlight the importance of mechanical stress determinants in the integrated set point for the knee (with individual variation), and how injury-related stress changes correlate with development of PTOA.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".