Structural Lesions Detected by Magnetic Resonance Imaging in the Spine of Patients with Spondyloarthritis - Definitions, Assessment System, and Reference Image Set
Bibliographic record
Abstract
Objective. There is no reliable and sensitive magnetic resonance imaging (MRI) assessment system for structural lesions in patients with spondyloarthritis (SpA). We sought to develop and illustrate a detailed anatomy-based set of MRI definitions and an assessment system for structural lesions in the spine of patients with SpA. Methods. MRI definitions of different structural (“chronic”) lesions at various anatomical locations in the spine, and an accompanying assessment system, were agreed by consensus within the Canada-Denmark MRI working group. Subsequently, a reference image set of representative examples of the individual pathologies, as well as borderline cases and important artefacts, were collected. Results. The defined lesions were (a) Bone erosions, subdivided into corner and non-corner vertebral body erosions and facet joint erosions; (b) Focal fat infiltration at vertebral corners; (c) Bone spurs, subdivided into corner and non-corner vertebral body spurs; and (d) Ankylosis, subdivided into corner and non-corner vertebral body ankylosis and facet joint ankylosis. All definitions were based on their appearance on sagittal T1-weighted MR images. Vertebral body structural lesions are assessed at each vertebral endplate at all 23 spinal levels from C2/3 to L5/S1, whereas facet joint lesions are to be assessed by segmental level (cervical, thoracic, and lumbar). Conclusion. An anatomy-based set of definitions and an assessment system for structural lesions in the spine of patients with SpA were developed and illustrated. The system is designed to study the spatial pattern of the lesions and their relation to spine inflammation and clinical and radiographic outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".