Anterior Chest Wall Involvement in Early Stages of Spondyloarthritis: Advanced Diagnostic Tools
Bibliographic record
Abstract
OBJECTIVE: Anterior chest wall (ACW) involvement is difficult to evaluate in patients with spondyloarthritis (SpA). Bone scan is sensitive to ACW involvement, while magnetic resonance imaging (MRI) detects early alterations in SpA. We compared the sensitivity and specificity of bone scans and MRI in assessing ACW in early SpA. METHODS: Out of 110 patients with early SpA attending the Outpatient Rheumatology Unit Clinic of Padua University from January 2008 to December 2010, the 40 complaining of pain and/or tenderness [60% with psoriatic arthritis (PsA), 12.5% with ankylosing spondylitis, and 27.5% with undifferentiated SpA] underwent bone scans and MRI. RESULTS: At clinical examination, sternocostoclavicular joints were involved in 87.5% on the right, 77.5% on the left, and 35% on the sternum. Bone scan was positive in 100% and MRI in 62.5% of these patients. Early MRI signs (bone edema, synovial hyperemia) were observed in 27.5%, swelling in 5%, capsular structure thickness in 37.5%, erosions in 15%, bone irregularities in 15%, osteoproductive processes in 12.5%, and osteophytes in 5%. A higher prevalence of Cw6, Cw7, B35, and B38 was found in 15%, 48%, 28%, and 12%, respectively, of the patients with PsA who had bone scans. CONCLUSION: Noted mainly in women, ACW involvement was frequent in early SpA. Both bone scans and MRI are useful in investigating ACW inflammation. Bone scans were found to have high sensitivity in revealing subclinical involvement, but a low specificity. MRI provides useful information for therapeutic decision making because it reveals the type and extent of the process. The significant associations of HLA-Cw6 and Cw7 with PsA could suggest that genetic factors influence ACW involvement.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".