Risk of Thrombosis in Sjögren Syndrome: The Open Question of Endothelial Function Immune-mediated Dysregulation
Bibliographic record
Abstract
Deep vein thrombosis (DVT) and pulmonary embolism (PE) are the 2 common clinical expressions of the vascular phenomenon of venous thromboembolism (VTE). VTE is a common complication during and soon after hospitalization for acute medical illness or surgery1. In Europe, it is estimated to account for 60,000 deaths per year2, and because PE accounts for 5%–10% of deaths in hospitalized patients, VTE is the most common preventable cause of inpatient death1. Historically, Virchow proposed 3 precipitants for venous thrombosis: venous stasis, increased coagulability of the blood, and damage to the vessel wall3. Patients with systemic inflammatory diseases are at high risk for VTE because inflammation is a key determinant of endothelial dysfunction in both arteries and veins, by changing the expression of selectins and cellular adhesion molecules4 and leading to a state of hypercoagulability by influencing clotting factor levels5. Thus, systemic inflammation likely contributes to increasing the risk of VTE in addition to the other known factors, such as age, obesity, immobilization, and malignancy, and it is a possible trigger factor for venous thrombus formation. Indeed, it is well known that an increased serum level of C-reactive protein is an additional risk factor for cardiovascular (CV) events6. In fact, patients with a chronic inflammatory condition, such as those with rheumatoid arthritis (RA) or other chronic inflammatory arthritides, show a higher risk of CV events than the general population7. Importantly, patients having an immune-mediated disease appear at risk of VTE, even independently from a detectable inflammatory state. In fact, in a … Address correspondence to Dr. L. Quartuccio, Clinic of Rheumatology, University of Udine, DAME, Piazzale S. Maria Misericordia 15, 33100 Udine, Italy. E-mail: luca.quartuccio{at}asuiud.sanita.fvg.it
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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 | 0.002 | 0.006 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".