The Basic Principles of Pathophysiology of Venous Thrombosis
Bibliographic record
Abstract
The past few decades have brought tremendous insight into the molecular and pathophysiological mechanisms responsible for thrombus generation. For a clinician, it is usually sufficient to explain the incident of deep vein thrombosis (DVT) with provoking factors such as trauma with vascular injury, immobilization, hormonal factors, or inherited or acquired coagulation defects. About half of DVTs are, however, lacking such triggers and are called unprovoked. Venous stasis and hypoxia at the valve sinus level may start a chain of reactions. The concept of immunothrombosis has added a new dimension to the old etiological triad of venous stasis, vessel wall injury, and changes in blood components. This is particularly important in COVID-19, where hyperinflammation, cytokines, and neutrophil extracellular traps are associated with the formation of microthrombi in the lungs. To better understand the mechanisms behind DVT and reach beyond the above-mentioned simplifications, animal models and clinical epidemiological studies have brought insight into the complex interplay between leukocytes, platelets, endothelium, cytokines, complements, and coagulation factors and inhibitors. These pathways and the interplay will be reviewed here, as well as the roles of cancer, anticancer drugs, and congenital thrombophilic defects on the molecular level in hypercoagulability and venous thromboembolism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".