Bone Structure and Perfusion Quantification of Bone Marrow Edema Pattern in the Wrist of Patients with Rheumatoid Arthritis: A Multimodality Study
Bibliographic record
Abstract
OBJECTIVE: To quantify bone structure and perfusion parameters in regions of bone marrow edema pattern (BMEP), non-edematous bone marrow (NBM), and pannus tissue areas in the wrists of patients with rheumatoid arthritis (RA) using 3-Tesla (3T) magnetic resonance imaging (MRI), and high resolution peripheral quantitative computed tomography (HR-pQCT). METHODS: Sixteen subjects fulfilling American College of Rheumatology classification were imaged using a HR-pQCT system and a 3T MRI scanner with an 8-channel wrist coil. Coronal T2-weighted and dynamic contrast-enhanced (DCE-MRI) images were acquired. BMEP and pannus tissue areas were segmented semiautomatically in T2-weighted images. NBM areas were placed at a similar distance from the joint space as BMEP regions. MR and HR-pQCT images were registered, and bone variables were calculated within the BMEP and NBM regions. Perfusion parameters in BMEP, pannus tissue, and NBM regions were calculated based on the signal-time curve obtained from DCE-MRI. RESULTS: Eighteen BMEP areas were segmented, 15 of them presented proximal to pannus-filled erosions. Significant increases in bone density and trabecular thickness and number were observed in all BMEP regions compared to NMB (p < 0.05). Significantly elevated perfusion measures were observed in both BMEP and pannus tissue regions compared to NBM (p < 0.05). CONCLUSION: BMEP regions showed significantly increased bone density and structures as well as perfusion measures, suggesting bone remodeling and active inflammation. Combining MRI and HR-pQCT provides a powerful multimodality approach for understanding BMEP and erosions, and for potentially identifying novel imaging markers for disease progression in RA.
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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 | 0.001 |
| 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".