Spinal Lesions in Axial Psoriatic Disease: How Should They Be Identified and Quantified by Magnetic Resonance Imaging?
Bibliographic record
Abstract
Proper assessment of patients with psoriatic arthritis (PsA) requires assessment of all disease domains, including axial disease. Magnetic resonance imaging (MRI) is the method of choice for evaluating axial involvement in PsA. When assessing patients with PsA for spinal involvement, it is important to assess both vertebral body lesions and posterolateral lesions, such as inflammation in facet joints and costovertebral joints, and enthesitis at spinous and transverse processes. The Canada-Denmark (CanDen) assessment system for spine MRIs is the preferred method for detailed evaluation of inflammation and structural damage at various anatomical locations in the spine, and it is reproducible and sensitive to change. The Assessment of Spondyloarthritis international Society (ASAS) has recently published MRI definitions of inflammatory and structural lesions in the spine, incorporating the CanDen definitions of spinal lesions on MRI. Applying the ASAS definitions and the CanDen assessment system in clinical practice and trials is recommended. Ongoing research/studies, not least the Axial Involvement in Psoriatic Arthritis (AXIS) study, may provide a data-driven definition of axial involvement in PsA. Ongoing research is expected to further improve and validate assessment tools for axial PsA and to provide a much-needed data-driven consensus-based definition of axial involvement in PsA.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".