3D morphometric analysis of calcified cartilage properties using micro-computed tomography
Bibliographic record
Abstract
Objective Our aim is to establish methods for quantifying morphometric properties of calcified cartilage (CC) from micro-computed tomography (μCT). Furthermore, we evaluated the feasibility of these methods in investigating relationships between osteoarthritis (OA), tidemark surface morphology and open subchondral channels (OSCCs). Method Samples ( n = 15) used in this study were harvested from human lateral tibial plateau ( n = 8). Conventional roughness and parameters assessing local 3-dimensional (3D) surface variations were used to quantify the surface morphology of the CC. Subchondral channel properties (percentage, density, size) were also calculated. As a reference, histological sections were evaluated using Histopathological osteoarthritis grading (OARSI) and thickness of CC and subchondral bone (SCB) was quantified. Results OARSI grade correlated with a decrease in local 3D variations of the tidemark surface (amount of different surface patterns ( r s = −0.600, P = 0.018), entropy of patterns (EP) ( r s = −0.648, P = 0.018), homogeneity index (HI) ( r s = 0.555, P = 0.032)) and tidemark roughness (TMR) ( r s = −0.579, P = 0.024). Amount of different patterns (ADP) and EP associated with channel area fraction (CAF) ( r p = 0.876, P < 0.0001; r p = 0.665, P = 0.007, respectively) and channel density (CD) ( r p = 0.680, P = 0.011; r p = 0.582, P = 0.023, respectively). TMR was associated with CAF ( r p = 0.926, P < 0.0001) and average channel size ( r p = 0.574, P = 0.025). CC topography differed statistically significantly in early OA vs healthy samples. Conclusion We introduced a μ-CT image method to quantify 3D CC topography and perforations through CC. CC topography was associated with OARSI grade and OSCC properties; this suggests that the established methods can detect topographical changes in tidemark and CC perforations associated with OA.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".