Patterns of Magnetic Resonance Imaging Bone Erosion in Rheumatoid Arthritis — Which Bones Are Most Frequently Involved and Show the Most Change?: Figure 1.
Bibliographic record
Abstract
OBJECTIVE: To investigate by magnetic resonance imaging (MRI) which bones in wrists and metacarpophalangeal (MCP) joints most frequently show bone erosions, and which most frequently demonstrate erosive progression, in early and established rheumatoid arthritis (RA). METHODS: MRI datasets from 258 RA patients [126 with early RA (disease duration < 6 months)] were analyzed, of whom 223, including 126 with early RA, had 1-year followup MRI. All patients had MRI of one wrist, whereas 86 patients had additional images of 2nd-5th MCP joints, and 46 patients additional images of the contralateral wrist. MRI were evaluated blinded by one reader, according to the OMERACT RA MRI scoring system (RAMRIS) for erosions, and presence/absence of erosions was noted in each bone, as was presence/absence of erosive progression. RESULTS: The capitate, ulna, lunate, triquetrum, and scaphoid were the 5 bones with both most frequent baseline erosions and most frequently demonstrated erosive progression. No bones were without erosions. Patterns of erosions and progression were similar in early and established RA. No major difference between dominant and nondominant wrists was detected. In the fingers, the 2nd-3rd MCP joint most frequently displayed erosions and erosive progression. CONCLUSION: The distribution and frequency of bone erosion and erosive progression as detected by MRI in RA wrists and MCP joints were identified. No pattern differences between early versus established disease and dominant versus nondominant sides were detected. No bones showed erosive progression. Thus, no self-evident simplification of the RAMRIS erosion score was identified. Bone involvement patterns may be considered, when joints are selected for MRI protocols for clinical trials and practice.
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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 | 0.002 | 0.001 |
| 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) | 0.005 | 0.002 |
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".