Discovertebral Erosions in Patients with Enteropathic Spondyloarthritis
Bibliographic record
Abstract
OBJECTIVE: Magnetic resonance imaging (MRI) is considered the modality of choice for the diagnosis of spondyloarthropathy (SpA)-related spondylodiscitis, or discovertebral erosions (DE). Our aim was to analyze the prevalence and the clinical features of DE in patients with enteropathic SpA (EA) using MRI. METHODS: We evaluated 72 patients with EA and 43 controls for the study. All patients and controls underwent rheumatological and gastroenterological clinical examinations, and demographic features were recorded. For each patient, these factors were also recorded: duration of inflammatory bowel disease and arthritis from onset to enrollment, history of viral and bacterial infections, and occurrence of previous major trauma to the spine. These scores were taken: Bath Ankylosing Spondylitis Metrology Index (BASMI), Bath Ankylosing Spondylitis Functional Index (BASFI), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Harvey-Bradshaw Index, and the Simple Clinical Colitis Activity Index. All subjects had MRI of the spine. RESULTS: On the basis of inclusion criteria, 43 patients with EA were included in the study. Twenty-three had axial EA (axEA) and 20 had axial and peripheral subset EA (overlap subset or peripheral type 3; axphEA). Twenty-two patients with EA (15/7 axEA/axphEA) showed DE (30.55%; p < 0.001). DE was significantly more prevalent in axEA subjects than in the overlap subset (p < 0.001). In axEA, DE had a significant direct correlation with arthritis duration (r = 0.546, p = 0.007). Patients with DE showed BASDAI, BASMI, and BASFI scores significantly higher than patients without DE (p < 0.001). CONCLUSION: We found a high prevalence of DE among patients with EA (30.55%), confirming that DE is an important characteristic aspect of SpA. We found a high prevalence in patients in the axphEA subset (31.82%), suggesting that DE could be a characterizing feature of the overlap subset.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".