Whole-Body Macrophage Positron Emission Tomography Imaging for Disease Activity Assessment in Early Rheumatoid Arthritis
Bibliographic record
Abstract
Objective To investigate the potential of whole-body positron emission tomography/computed tomography (PET/CT) with a macrophage tracer to image arthritis in patients with early rheumatoid arthritis (RA). Methods Thirty-five previously untreated, clinically active patients with early RA underwent whole-body PET/CT scanning with the macrophage tracer (R)-[11C]PK11195 in addition to clinical assessment (Disease Activity Score in 44 joints [DAS44]). Tracer uptake was assessed quantitatively as standardized uptake values (SUVs). In addition, 2 readers blinded to clinical assessment visually scored tracer uptake in joints. Clinical and PET variables were compared using Cohen , linear regression/correlation, and t tests, where appropriate. Results All but 1 patient showed enhanced tracer uptake in at least 1 joint. Twelve percent of all joints (171/1470) were visually positive on the PET scan, most frequently the small joints in feet (40%) and hands (37%), followed by wrists (15%). Correlations of visual scores with clinical findings both at patient and joint levels were absent or weak. In contrast, average SUVs in the hands, feet, and whole body showed significant correlations with DAS44 scores, with the best correlation seen in the feet (R2 = 0.29, P < 0.01). Conclusion Clinically active patients with early RA had increased joint uptake of a macrophage PET tracer, especially in the feet. Quantitative, but not visual PET measures of whole body and joint groups, particularly the feet, showed moderate and statistically significant correlations with clinical outcome.
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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 | 0.000 | 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".