Determining In-Vivo Human Tibiofemoral Cartilage Stiffness Using Dual Fluoroscopy and Magnetic Resonance Imaging
Bibliographic record
Abstract
Anterior cruciate ligament deficiency (ACLD) dramatically increases the risk of knee osteoarthritis (OA). Currently, there is no clinical diagnostic to predict joint degeneration in pre-radiographic OA. The purpose of this research is to develop methods to combine deformation and joint force estimates to determine changes in cartilage stiffness in pre-radiographic OA. Preliminary data were collected for ACLD (n=4, >5 year history) and healthy control (n=5, CON) subjects. 3D bone and cartilage models were created in Amira (FES, Germany) from magnetic resonance (MR) data obtained (GE 3T Discovery 750, USA). Dual-fluoroscopy (DF) images were collected in conjunction with ground reaction forces (GRF)(Bertec, USA) during standing weight bearing. Bone kinematics were determined in Autoscoper (Brown University, USA) and applied to cartilage models. Deformations were quantified as the change in median proximity of model faces (Matlab 2015b, MathWorks, USA). Cartilage deformation was higher in ACLD compared to the CON subject, indicating a reduced ability to resist compressive loading. These results provide proof of concept that cartilage stiffness changes in pre-radiographic OA and in-vivo DF/MR measure is sensitive to alterations in load deformation. Vertical GRF provided physiological loading rate data for analysis with the viscoelastic cartilage deformation response. This MSc will use inverse dynamics to estimate knee joint forces and load-deformation curve-fitting approaches to determine a model for cartilage stiffness mechanics. This research supports the development of novel clinical diagnostics for pre-radiographic OA. [1] Lohmander et al. 2007 Am. J. Sports Med. 35(10): 1756–1769. [2] Calvo et al. 2004 OA Cart. 12(11): 878–86.
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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 | 0.000 | 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 | 0.000 |
| 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".