Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is caused by heparin-dependent, platelet-activating IgG antibodies that increase thrombin generation in vivo, producing a prothrombotic phenotype. In addition to platelet activation, there is in vitro evidence that activation of endothelium and monocytes occurs, perhaps directly by HIT antibodies, but more likely through activated platelet (or microparticle)-endothelial-leukocyte interactions. Patients with cardiac disease receiving heparin present important diagnostic and therapeutic issues when unexpected thrombocytopenia arises. Concomitant vascular disease burden and intravascular catheter use further increase risk of HIT-associated arterial thrombosis in this patient population. Whether arterial thrombosis simply reflects the "hypercoagulability state" of HIT interacting with diseased or injured arteries, or whether arterial "white clots" reflect additional prothrombotic effects of HIT via endothelial and monocyte activation, remains uncertain. Patients with HIT can also develop deep-vein thrombosis, which can progress to limb loss if coumarin (warfarin) leads to severe protein C depletion (coumarin-induced venous limb gangrene). Therapy for patients strongly suspected to have HIT should focus on inhibiting thrombin (or its generation) pharmacologically. Two direct thrombin inhibitors (lepirudin, argatroban) are approved for treating HIT. When using these agents, coumarin anticoagulation should be delayed pending substantial resolution of thrombocytopenia, before cautiously introducing overlapping coumarin therapy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".