Contrast‐enhanced computed tomography (CECT) attenuation is associated with stiffness of intact knee cartilage
Bibliographic record
Abstract
Contrast-enhanced computed tomography (CECT) using charged contrast-agents enables quantification of cartilage glycosaminoglycan content. Since glycosaminoglycan content is a key determinant of cartilage compressive stiffness, CECT measurements have the potential to non-invasively assess cartilage stiffness. The objective of this study was to determine whether CECT attenuation, using a cationic contrast-agent (CA4+), correlates with the stiffness of intact cartilage. Six fresh femoral and six fresh tibial compartments with intact cartilage were obtained from patients undergoing total knee replacement surgery. The instantaneous stiffness was determined for 25-50 points on the surface of each compartment using an established indentation technique. The samples were then immersed in CA4+ solution for 48 h, scanned in a micro-CT scanner, and the average CECT attenuation at each indentation site was found for the superficial cartilage. A significant (p < 0.01) and positive correlation was observed between stiffness and CECT attenuation for sites from each individual cartilage surface, with correlation coefficients ranging from r = 0.37-0.57 and r = 0.48-0.69 (p < 0.01) for the tibia and femur, respectively. When data for each type of cartilage surface were pooled together, the correlation coefficients were r = 0.73 for femoral condyle data points and r = 0.49 for tibial plateau data points. CECT provided a map of cartilage stiffness across each surface, which allows regions of low stiffness to be identified. These findings support continued evaluation and development of quantitative imaging techniques to assess the functional properties of cartilage. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2641-2647, 2018.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".