Clinical and ultrasonographic enthesitis assessment before and after anti-tumor necrosis factor treatment in patients with spondyloarthritis
Bibliographic record
Abstract
Objectives: This study aimed to clinically and ultrasonographically evaluate enthesitis in patients with spondyloarthritis (SpA) and to determine enthesitis response to anti-tumor necrosis factor (TNF) treatment. Patients and methods: Thirty-one SpA patients (22 males, 9 females; mean age: 39.4±10.9 years; range, 22 to 60 years) who started anti-TNF treatment due to their high disease activity were included in the cross-sectional prospective study between May 2017 and January 2018. Ankylosing Spondylitis Disease Activity Score, Bath Ankylosing Spondylitis Disease Activity Index, Ankylosing Spondylitis Quality of Life Questionnaire, Bath Ankylosing Spondylitis Functional Index, and Bath Ankylosing Spondylitis Metrology Index were recorded. Maastricht Ankylosing Spondylitis Enthesitis Score (MASES) and Spondyloarthritis Research Consortium of Canada (SPARCC) Enthesitis Score were utilized for clinical enthesitis evaluation. Patients were ultrasonographically evaluated in accordance with the Madrid Sonographic Enthesitis Index (MASEI) by a blinded sonographer. Patients were clinically and ultrasonographically assessed at baseline and in the third month after the treatment. Results: In the initial evaluation, 24 (77.42%) of the patients had clinical enthesitis, and 30 (96.77%) of the patients had ultrasonographic enthesitis. After anti-TNF treatment, MASES, SPARCC, MASEI-structure, MASEI-thickness, MASEI-bursitis, MASEI-Doppler, MASEI-inflammatory, and MASEI-total scores significantly decreased (p<0.05). There was no significant change in MASEI-damage, MASEI-erosion, and MASEI-calcification scores following the therapy (p>0.05). Conclusion: Anti-TNF treatment may improve clinical and ultrasonographic enthesitis, particularly inflammatory changes. Erosions and calcifications may not ameliorate after three months of anti-TNF treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".