Radiographic Evidence of Hip Joint Recovery in Patients with Ankylosing Spondylitis after Treatment with Anti-tumor Necrosis Factor Agents: A Case Series
Bibliographic record
Abstract
To the Editor: Ankylosing spondylitis (AS) is a chronic inflammatory disease of the axial skeleton and large peripheral joints. The efficacy of tumor necrosis factor (TNF) antagonists in the treatment of AS has been documented in several metaanalyses of randomized trials, in which significant improvements in disease activity were observed1. However, there is no evidence that anti-TNF-α agents prevent radiographic progression in patients with AS. In rheumatoid arthritis (RA), anti-TNF-α agents can prevent bone erosion and radiographic progression by suppressing osteoclast activity2,3. Unlike new bone formation in the axial spine, synovial inflammation within the hip joint induces bone erosion and subsequent joint destruction4,5 in AS. Additionally, the histological appearance of the synovial membrane of the hip joint in patients with AS is similar to that observed in RA6. These results suggest that both osteoproliferative and osteodestructive changes may occur in parallel at different sites in AS. In the present case series, we reviewed 6 patients with AS whose hip joint space … Address correspondence to Professor S.H. Lee, Division of Rheumatology, Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, School of Medicine, Kyung Hee University, 892 Dongnam-ro, Gangdong-gu, Seoul 134-727, Republic of Korea. E-mail: boltaguni{at}yahoo.co.kr
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".