Quantifying Complex Micro‐Topography of Degenerated Articular Cartilage Surface by Contrast‐Enhanced Micro‐Computed Tomography and Parametric Analyses
Bibliographic record
Abstract
ABSTRACT One of the earliest changes in osteoarthritis (OA) is a surface discontinuity of the articular cartilage (AC), and these surface changes become gradually more complex with OA progression. We recently developed a contrast enhanced micro‐computed tomography (μCT) method for visualizing AC surface in detail. The present study aims to introduce a μCT analysis technique to parameterize these complex AC surface features and to demonstrate the feasibility of using these parameters to quantify degenerated AC surface. Osteochondral plugs (n = 35) extracted from 19 patients undergoing joint surgery were stained with phosphotungstic acid and imaged using μCT. The surface micro‐topography of AC was analyzed with developed method. Standard root mean square roughness (Rq) was calculated as a reference, and the Area Under Curve (AUC) for receiver operating characteristic analysis was used to compare the acquired quantitative parameters with semi‐quantitative visual grading of μCT image stacks. The parameters quantifying the complex micro‐topography of AC surface exhibited good sensitivity and specificity in identifying surface continuity (AUC: 0.93, [0.80 0.99]), fissures (AUC: 0.94, [0.83 0.99]) and fibrillation (AUC: 0.98, [0.88 1.0]). Standard Rq was significantly smaller compared with the complex roughness (CRq) already with mild surface changes with all surface reference parameters − continuity, fibrillation, and fissure sum. Furthermore, only CRq showed a significant difference when comparing the intact surface with lowest fissure sum score. These results indicate that the presented method for evaluating complex AC surfaces exhibit potential to identify early OA changes in superficial AC and is dynamic throughout OA progression. © 2019 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodicals, Inc. on behalf of the Orthopaedic Research Society. Society. 9999:1–12, 2019.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".