The predictive value of markers of fibrinolysis and endothelial dysfunction in the post thrombotic syndrome
Bibliographic record
Abstract
The post thrombotic syndrome (PTS) develops in 20-40% of deep venous thrombosis (DVT) patients. Risk factors for PTS have not been well elucidated. Identification of risk factors would facilitate individualised risk assessment for PTS. We conducted a systematic review to determine whether biomarkers of fibrinolysis or endothelial dysfunction can predict the risk for PTS among DVT patients. Studies were identified by searching the electronic databases PubMed, EMBASE, Scopus and Web of science. We included studies published between 1990 and 2013, measured biomarker levels in adult DVT patients, and reported rates of PTS development. Fourteen studies were included: 11 investigated the association between D-dimer and PTS; three examined fibrinogen; two measured von Willebrand factor; one measured plasminogen activator inhibitor-1; one assessed ADAMTS-13 (A Disintegrin and Metalloprotease with Thrombospondin type 1 repeats) and one measured factor XIII activity. Studies varied with regards to inclusion criteria, definition of PTS, time point and method of biomarker measurement. We were unable to meta-analyse results due to marked clinical heterogeneity. Descriptively, a significant association with PTS was found for D-dimer in four studies and factor XIII in one study. Further prospective research is needed to elucidate whether these markers might be useful to predict PTS development.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".