Magnetic Resonance Imaging — Key to Understanding and Monitoring Disease Progression in Spondyloarthritis?
Bibliographic record
Abstract
Understanding the process of structural progression in axial spondyloarthritis (axSpA) and elucidating the link/dissociation between inflammation and damage have attracted much attention, as has the question of whether avoiding structural progression is therapeutically possible. The impressive effect of tumor necrosis factor (TNF) inhibitors on disease activity without any apparent effect on structural damage progression as assessed by the standard measure (radiography) has fueled this interest1,2. Is the link between inflammation and damage nonexistent? Is it time-dependent (only occurring at a specific timepoint, e.g., early in the disease or only after long-lasting therapy)? Or is the apparent lack of a link the result of inadequate methods for measuring progression? A sufficiently sensitive and reliable method for demonstrating change is a prerequisite to study structural progression. Preferably, different aspects of progression should be measurable, particularly if there is an interest in understanding details of the disease process. In this issue of The Journal , Maksymowych, et al describe the development and validation of a magnetic resonance imaging (MRI) scoring method for several aspects of structural damage in the sacroiliac joints (SIJ)3. This is an important step in clarification of the development of these structural changes. Building on definitions of individual SIJ pathologies (erosion, backfill, fat infiltration, ankylosis) described in the MORPHO study by Weber, et al 4,5, Maksymowych, et al describe and apply a newly developed scoring system by which the presence/absence of lesions is systematically scored in SIJ quadrants (fat, erosion) or halves (backfill, ankylosis) separately in each of 5 consecutive semicoronal slices through the cartilaginous part of the joint3. The quadratic per-slice scoring system follows the same principles as the Spondyloarthritis Research Consortium of Canada (SPARCC) scoring method for SIJ inflammation6. The structural damage scoring system is … Address correspondence to Prof. M. Østergaard, Copenhagen Center for Arthritis Research, Center for Rheumatology and Spine Diseases, Copenhagen University Hospital Glostrup, Nordre Ringvej 57, DK-2600 Glostrup, Denmark; E-mail: mo{at}dadlnet.dk
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".