Seronegative Sjögren Syndrome: A Forgotten Entity?
Bibliographic record
Abstract
As a highly varied disease, Sjögren syndrome (SS; also termed Sjögren disease ) is estimated to affect 0.01% to 0.72% of the population, with an overwhelming bias toward the female gender.1 SS causes a significant burden to the quality of life of patients and inhibits their function. Patients present with a spectrum of clinical manifestations from sicca (dryness) symptoms to potentially severe extraglandular and/or systemic features, such as inflammatory arthritis, interstitial lung disease, neurological dysfunction, cryoglobulinemia, and malignant lymphoma. The hallmark of SS is B cell hyperreactivity and consequently, autoantibodies such as antinuclear antibodies (ANAs; including anti-Ro60/Ro52/La) and rheumatoid factors.2 These autoantibodies are present in 45% to 75% of patients.3 When patients lack serum anti-Ro52/Ro60 (SSA/Ro), the diagnosis of SS relies upon meeting internationally defined classification criteria,4 which include reduction of measured tear and/or salivary flow, and the finding of focal lymphocytic sialadenitis on minor salivary gland biopsy (MSGB). Patients fulfilling SS criteria who do not express classic serum antibodies are called patients with seronegative SS. However, there is no universally accepted definition of which autoantibodies should define seropositive from seronegative SS. Hence, the proportion of patients with seronegative SS varies in the literature, ranging from 8% to 37% of SS cohorts.5-7 Because of the lack of SS-associated autoantibody biomarkers, seronegative SS may be missed in the clinic if further investigations such as an MSGB are … Address correspondence to Dr. A.Y.S. Lee, Centre for Immunology & Allergy Research, Westmead Institute for Medical Research, 176 Hawkesbury Road, Westmead, NSW 2145, Australia. Email: adrian.lee1{at}sydney.edu.au.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".