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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".