Increased levels of histidine-rich glycoprotein are associated with the development of post-thrombotic syndrome
Bibliographic record
Abstract
Denser fibrin networks which are relatively resistant to lysis can predispose to post-thrombotic syndrome (PTS). Histidine-rich glycoprotein (HRG), a blood protein displaying antifibrinolytic properties, is present in fibrin clots. We investigated whether HRG may affect the risk of PTS in relation to alterations to fibrin characteristics. In venous thromboembolism (VTE) patients, we evaluated plasma HRG levels, plasma clot permeability, maximum absorbance, clot lysis time and maximum rate of increase in D-dimer levels released from clots after 3 months of the index event. We excluded patients with cancer and severe comorbidities. After 2 years of follow-up, 48 patients who developed PTS had 18.6% higher HRG at baseline. Baseline HRG positively correlated with clot lysis time, maximum absorbance, and thrombin-activatable fibrinolysis inhibitor (TAFI) activity but was inversely correlated with plasma clot permeability and maximum rate of increase in D-dimer levels released from clots. On multivariate regression model adjusted for age, fibrinogen and glucose, independent predictors of PTS were recurrent VTE, baseline HRG level, and TAFI activity. VTE recurred in 45 patients, including 30 patients with PTS, and this event showed no association with elevated HRG. Our findings suggest that increased HRG levels might contribute to the development of PTS, in part through prothrombotic fibrin clot properties.
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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 | 0.001 | 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".