Bibliographic record
Abstract
Platelet factor 4 (PF4) is a highly cationic tetrameric protein that can be targeted by platelet-activating anti-PF4 antibodies of immunoglobulin G (IgG) class. Certain features of PF4, including its multivalent nature (duplicate antigen sites per tetramer), the ability of many PF4 tetramers to undergo close approximation through charge neutralization, and the dimeric binding of IgG molecules, results in formation of IgG-containing immune complexes in situ on platelets, neutrophils, and monocytes, resulting in Fcγ receptor-mediated pancellular activation that also activates hemostasis (potential for disseminated intravascular coagulation). This review discusses 4 anti-PF4 disorders: classic heparin-induced thrombocytopenia ([HIT]; triggered by heparin and certain other polyanionic pharmaceuticals, featuring predominantly heparin-dependent antibodies), autoimmune HIT (aHIT; severe subtype of HIT that features both heparin-dependent and heparin-independent platelet-activating antibodies), and spontaneous HIT (non-heparin triggers such as knee replacement surgery and infection; predominantly heparin-independent platelet-activating antibodies). Most recently, a novel fourth anti-PF4 disorder, vaccine-induced immune thrombotic thrombocytopenia (VITT), was identified as an ultrarare complication of adenovirus vector vaccines. VITT is characterized by thrombocytopenia, disseminated intravascular coagulation, a high frequency of thrombosis-including in unusual sites (cerebral veins, splanchnic veins)-and highly pathogenic anti-PF4 antibodies with heparin-independent platelet-activating 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".