Predicting Sjogren's syndrome in patients with recent-onset SLE
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of SS in a cohort of recent-onset SLE patients and evaluate the clinical and immunological variables that may identify SLE patients prone to develop SS. METHODS: A total of 103 patients participating in a prospective cohort of recent-onset SLE were assessed for fulfilment of the American European Consensus Group criteria for SS using a three-phase approach: screening (European questionnaire, Schirmer-I test and wafer test), confirmation (fluorescein staining test, non-stimulated whole-salivary flow and anti-Ro/La antibodies) and lip biopsy. Anti-Ro/SSA and anti-La/SSB antibodies and RF were measured at entry into the cohort and at SS assessment. RESULTS: Ninety-three females and 10 males were included. Mean age at lupus diagnosis was 25.9 ± 8.9 years, and lupus duration at SS assessment was 30.9 ± 9.1 years. SS was diagnosed in 19 (18.5%) patients, all female, and the patients were older at SLE diagnosis than patients without SS (30.8 ± 9.3 vs 24 ± 8.8 years, P = 0.004). Anti-Ro/SSA antibody was more common in SLE-SS patients (84% vs 55%, P = 0.02, LR + 1.53, 95% CI 1.14, 2.04). In the multivariate analysis, age ≥25 years and anti-Ro/SSA antibodies at SLE diagnosis were identified as predictors of SLE-SS, while the absence of anti-Ro/SSA, anti-La/SSB and RF seems to be protective (LR- 0.14, 95% CI 0.02, 0.95). CONCLUSION: The overlap of SLE and SS occurs in almost one-fifth of SLE patients and presents early during its evolution. SLE onset at age ≥25 years plus the presence of anti-Ro/SSA antibody at diagnosis are useful predictors, while the absence of anti-Ro/SSA, anti-La/SSB and RF identifies patients at lowest risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".