IgA antibodies to myeloperoxidase in patients with eosinophilic granulomatosis with polyangiitis (Churg-Strauss).
Bibliographic record
Abstract
OBJECTIVES: To better understand the real-world characteristics and costs of Sjögren's syndrome (SS). METHODS: Analysing the MarketScan Commercial Claims database from Jan. 1, 2006 to Dec. 31, 2011, we identified 10,414 patients ≥18 years old newly diagnosed with SS. Patient characteristics, drugs (commonly used for SS), resource utilisation, and medical costs were evaluated for 12 months pre- and post-diagnosis. RESULTS: Mean age was 55 years; 90% were female. At diagnosis, SS patients were most often seen by rheumatologists (39%) or internists (14.2%); the most common concurrent autoimmune conditions were rheumatoid arthritis (17.9%) and systemic lupus erythematosus (14.6%). Other common comorbidities were hypertension (37.6%), osteoarthritis (31.4%), and hyperlipidaemia/dyslipidaemia (30.3%). Post diagnosis of SS, claims for myocardial infarction and coronary artery bypass graft doubled. Medications of interest prescribed post-diagnosis were eye/mouth drugs (32.2%) and synthetic immunosuppressants (32.1%). Biologic drugs were prescribed to a minority (TNF inhibitors, ~5.0%; non-TNF inhibitors, 1%). Of note, prescriptions for all systemic immunotherapies (synthetic and biologic) were significantly lower in the subgroup without concurrent autoimmune disease, and 15.1% of the overall population had no SS-related prescriptions. Post diagnosis, total medical resource utilisation and total medical costs increased (1.2 and 1.4-fold, respectively). CONCLUSIONS: In this retrospective, real-world analysis, medical claims in the first year after SS diagnosis revealed that cardiovascular (CV) events increased and all-cause healthcare costs grew by 40%. Pharmacologic management consisted primarily of low potency immunomodulation and symptomatic treatments. Systemic disease-modifying therapies were used mostly in patients who had another concurrent autoimmune disease, suggesting a lack of treatment options for SS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".