Levels of Inflammatory Markers and the Development of the Post Thrombotic Syndrome.
Bibliographic record
Abstract
Abstract Background: The post-thrombotic syndrome (PTS) occurs frequently after deep vein thrombosis (DVT) despite appropriate anticoagulant therapy. A close relationship between inflammation and thrombosis exists. While the inflammatory process at the time of DVT appears to improve thrombus resolution, it may promote destruction of venous valves, valvular reflux and subsequent development of PTS. Objective: We prospectively evaluated the association between levels of four cytokines (IL-6, IL-8, IL-10 and MCP-1), two adhesion molecules (ICAM-1 and VCAM-1) and the development of PTS in a well-defined cohort of patients with DVT. Methods: The study population consisted of 387 patients with objectively diagnosed symptomatic DVT who participated in the Venous Thrombosis Outcomes (VETO) Study and were followed for two years to determine the incidence of PTS. For this substudy, plasma samples frozen at the 4 month visit in 307 study patients who consented to provide blood samples were thawed and analyzed for the above inflammatory markers using the Luminex beads technology. Associations between marker levels and development of the PTS during follow-up were evaluated. Results: Mean levels of IL-6 were significantly higher in patients with PTS compared to patients without PTS (7.35 pg/ml ± 14.26 vs. 4.60 pg/ml ± 4.90;p=0.03). Logistic regression analyses showed significant associations between PTS and levels above vs. below the median of IL-6 (OR 1.66; 95% CI 1.05, 2.62 (p=0.03)) and ICAM-1 (OR 1.63; 95% CI 1.03, 2.58 (p=0.04)). None of the other markers showed any association with PTS. Conclusion: Our study suggests the presence of significant associations between markers of inflammation such as IL-6 and ICAM-1 and the development of PTS. Further work is needed to evaluate this relationship and to analyze other candidate markers that could be implicated etiologically in the association between DVT and PTS. If confirmed, this could lead to identification of new therapeutic targets for preventing PTS after DVT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".