Prevalence of Autoantibodies in SAPHO Syndrome: A Single-center Study of 90 Patients
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of the most often tested autoantibodies in synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. METHODS: We identified 90 patients seen in our unit between June 2002 and June 2009, and diagnosed according to the proposed criteria for SAPHO syndrome. Demographic and clinical data were collected as well as immunological results, including antinuclear, antithyroid peroxydase (TPO), antithyroid globulin (Tg), antigastric parietal cell, antismooth muscle, antimitochondria, and anti-liver-kidney microsome (LKM) antibodies. Anticyclic citrullinated peptide (CCP) antibodies were analyzed in 69 patients, antibodies to soluble extractable nuclear antigens in 43, anti-double-stranded DNA (dsDNA) antibodies in 22 [depending on the type of fluorescence of antinuclear antibody (ANA)], and antiendomysium antibodies in 55. RESULTS: Autoantibodies were found in 20 patients (22.2%): 14 patients (15.5%) had positive ANA (titer >/= 1/160); among them, 10 (11%) patients never took a lupus-inducing drug. Antithyroid antibodies (anti-TPO and/or anti-Tg antibodies) were found in only 3 patients (3.3%). Three patients (3.3%) were positive for antigastric parietal cell antibodies and 4 (4.4%) were weakly positive for antismooth muscle antibodies. Antimitochondria and LKM antibodies were negative in all 90 patients. Anti-CCP and anti-dsDNA antibodies were negative in the 69 and 22 patients tested, respectively. One out of 43 patients (2.3%) had anti-SSA antibodies. Antiendomysium antibodies were negative in the 55 patients tested. CONCLUSION: Our study indicates an increased prevalence of autoantibodies in SAPHO syndrome, with no specific profile. We failed to confirm the reports of an increased prevalence of antithyroid antibodies. These results tend to support a link between autoimmunity and SAPHO syndrome.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".