Diffusion-weighted Imaging Is a Sensitive and Specific Magnetic Resonance Sequence in the Diagnosis of Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: We tested the discriminatory capacity of diffusion-weighted magnetic resonance imaging (DWI) and its potential as an objective measure of treatment response to tumor necrosis factor inhibition in ankylosing spondylitis (AS). METHODS: Three cohorts were studied prospectively: (1) 18 AS patients with Bath Ankylosing Spondylitis Disease Activity Index > 4, and erythrocyte sedimentation rate > 25 and/or C-reactive protein > 10 meeting the modified New York criteria for AS; (2) 20 cases of nonradiographic axial spondyloarthritis (nr-axSpA) as defined by the Assessment of Spondyloarthritis international Society (ASAS) criteria; and (3) 20 non-AS patients with chronic low back pain, aged between 18 and 45 years, who did not meet the imaging arm of the ASAS criteria for axSpA. Group 1 patients were studied prior to and following adalimumab treatment. Patients were assessed by DWI and conventional magnetic resonance imaging (MRI), and standard nonimaging measures. RESULTS: At baseline, in contrast to standard nonimaging measures, DWI apparent diffusion coefficient (ADC) values showed good discriminatory performance [area under the curve (AUC) > 80% for Group 1 or 2 compared with Group 3]. DWI ADC values were significantly lower posttreatment (0.45 ± 0.433 before, 0.154 ± 0.23 after, p = 0.0017), but had modest discriminating capacity comparing pre- and posttreatment measures (AUC = 68%). This performance was similar to the manual Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system. CONCLUSION: DWI is informative for diagnosis of AS and nr-axSpA, and has moderate utility in assessment of disease activity or treatment response, with performance similar to that of the SPARCC MRI score.
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 | 0.002 |
| 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".