T2 Mapping as a New Method for Quantitative Assessment of Cartilage Damage in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is associated with damage of the articular cartilage and the periarticular bone. While imaging of bone damage has substantially improved in recent years, direct imaging of the articular cartilage of the hand joints in patients with RA is still challenging. The study used T2 mapping of the finger joints to assess cartilage damage in RA. METHODS: Magnetic resonance imaging (MRI) at 3 Tesla was done in 30 patients with RA, and T2 relaxation times visualizing alteration in the collagen network and hydration of articular cartilage were mapped in 6 cartilage regions of the metacarpophalangeal (MCP) joints 2 and 3. Values were related to autoantibody status [anticitrullinated protein antibodies (ACPA), rheumatoid factor (RF)], disease duration, and disease activity as well as sex and age of the patients. RESULTS: T2 relaxation times could be reliably measured in the 6 regions of the MCP joints. Significantly higher relaxation times indicating more advanced cartilage alterations were observed in the metacarpal heads of ACPA-positive (p = 0.001-0.010) and RF-positive patients (p = 0.013-0.025) as well as those with longer disease duration (> 3 yrs; p = 0.028-0.043). Current disease activity, sex, and age did not influence T2 relaxation times. CONCLUSION: These data show that cartilage damage can be localized and quantified in the hand joints of patients with RA by T2 mapping. Further, ACPA and RF positivity as well as disease duration appear to be the crucial factors influencing cartilage damage.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".