Bibliographic record
Abstract
In recent years, the traditional view of the hemostatic system as being regulated by a coagulation factor cascade coupled with platelet activation has been increasingly challenged by new evidence that activation of the immune system strongly influences blood coagulation and pathological thrombus formation. Leukocytes can be induced to express tissue factor and release proinflammatory and procoagulant molecules such as granular enzymes, cytokines, and damage-associated molecular patterns. These mediators can influence all aspects of thrombus formation, including platelet activation and adhesion, and activation of the intrinsic and extrinsic coagulation pathways. Leukocyte-released procoagulant mediators increase systemic thrombogenicity, and leukocytes are actively recruited to the site of thrombus formation through interactions with platelets and endothelial cell adhesion molecules. Additionally, phagocytic leukocytes are involved in fibrinolysis and thrombus resolution, and can regulate clearance of platelets and coagulation factors. Dysregulated activation of leukocyte innate immune functions thus plays a role in pathological thrombus formation. Modulation of the interactions between leukocytes or leukocyte-derived procoagulant materials and the traditional hemostatic system is an attractive target for the development of novel antithrombotic strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".