Hemostatic and Fibrinolytic Changes Are Related to Inflammatory Conditions in Patients with Psoriatic Arthritis — Effect of Different Treatments
Bibliographic record
Abstract
OBJECTIVE: To prospectively evaluate the effect of tumor necrosis factor (TNF)-α inhibitors on hemostatic and fibrinolytic variables in subjects with psoriatic arthritis (PsA). METHODS: Among subjects with PsA who were taking traditional disease-modifying antirheumatic drugs (DMARD), 98 patients with active disease who switched to treatment with TNF-α inhibitors were enrolled in this study (Group 1). In parallel, 98 matched subjects with minimal disease activity (MDA) and treated with DMARD were enrolled (Group 2). In all patients, hemostatic and fibrinolytic variables were evaluated at enrollment and after a 6-month followup. Results were stratified according to treatment and to MDA achievement. RESULTS: Seventy-six Group 1 and 80 Group 2 subjects completed the 6-month followup. During the followup, significant changes in hemostatic and fibrinolytic variables were found in Group 1, but not in Group 2 subjects. At the end of the followup, patients treated with TNF-α inhibitors showed significantly lower levels of hemostatic and fibrinolytic variables as compared to those treated with traditional DMARD. Among Group 1 subjects, changes in hemostatic and fibrinolytic variable levels were significantly higher in those who achieved MDA versus in those who did not. Multivariate analyses showed that a treatment with TNF-α blockers affected fibrinolytic variables [plasminogen activator inhibitor-1 (PAI-1), tissue plasminogen activator (t-PA)] and some acute-phase proteins (D-dimer, coagulation factor VIII, and von Willebrand factor). In contrast, the MDA achievement during treatment with TNF-α blockers maximally affected fibrinolytic variables (PAI-1 and t-PA). CONCLUSION: TNF-α inhibitors brought about a significant improvement of hemostatic and fibrinolytic balance in subjects with PsA. Maximal changes were found in patients achieving MDA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".