The instrumented sheep knee to elucidate insights into osteoarthritis development and progression: A sensitive and reproducible platform for integrated research efforts
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis of the knee is a very common condition that has been difficult to treat. The majority of cases are considered idiopathic. Much research effort remains focused on biology rather than the biomechanics of such joints. Some new methods were developed and validated to better appreciate the subtleties of the biomechanical integrity of joints, and how changes in biomechanics can contribute to osteoarthritis. METHODS: Over the past 15 years our lab has enhanced the sensitivity of the assessment of knee biomechanics of an instrumented, trained large animal model (sheep) of osteoarthritis and integrated the findings with biological and histological assessments. These new methods include gait analysis before and after injury followed by robotic validation post-sacrifice, and more recently using Fibre Bragg Grating sensors to detect alterations in cartilage stresses. RESULTS: A review of the findings obtained with this model are presented. The findings indicate that sheep, like humans, exhibit individual characteristics. They also indicate that joint kinetics, rather than kinematics may better define the alterations induced by injury. With the addition of Fibre Bragg Grating sensors, it has been possible to measure with good accuracy, alterations to cartilage stresses following a controlled knee injury. INTERPRETATION: Using this model as Proof of Concept, this sheep system can now be viewed as a sensitive platform to address many questions related to risk for development of idiopathic osteoarthritis of the human knee, the efficacy of potential interventions to correct biomechanical disruptions, and how joint biomechanics and biology are integrated during aging.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".