Anti-PF4 immunothrombosis without proximate heparin or adenovirus vector vaccine exposure
Bibliographic record
Abstract
ABSTRACT: Platelet-activating anti-platelet factor 4 (PF4)/heparin antibodies and anti-PF4 antibodies cause heparin-induced thrombocytopenia (HIT) and vaccine-induced immune thrombocytopenia and thrombosis (VITT), respectively. Diagnostic and treatment considerations differ somewhat between HIT and VITT. We identified patients with thrombocytopenia and thrombosis without proximate heparin exposure or adenovirus-based vaccination who tested strongly positive by PF4/polyanion enzyme-immunoassays and negative/weakly positive by heparin-induced platelet activation (HIPA) test but strongly positive by PF4-induced platelet activation (PIPA) test (ie, VITT-like profile). We tested these patients by a standard chemiluminescence assay that detects anti-PF4/heparin antibodies found in HIT (HemosIL AcuStar HIT-IgG(PF4-H)) as well as a novel chemiluminescence assay for anti-PF4 antibodies found in VITT. Representative control sera included an exploratory anti-PF4 antibody-positive but HIPA-negative/weak cohort obtained before 2020 (n = 188). We identified 9 patients with a clinical-pathological profile of a VITT-like disorder in the absence of proximate heparin or vaccination, with a high frequency of stroke (arterial, n = 3; cerebral venous sinus thrombosis, n = 4), thrombocytopenia (median platelet count nadir, 49 × 109/L), and hypercoagulability (greatly elevated D-dimer levels). VITT-like serological features included strong reactivity by PIPA (aggregation <10 minutes in 9/9 sera) and positive testing in the novel anti-PF4 chemiluminescence assay (3/9 also tested positive in the anti-PF4/heparin chemiluminescence assay). Our exploratory cohort identified 13 additional patient sera obtained before 2020 with VITT-like anti-PF4 antibodies. Platelet-activating VITT-like anti-PF4 antibodies should be considered in patients with thrombocytopenia, thrombosis, and very high D-dimer levels, even without a proximate exposure to heparin or adenovirus vector vaccines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".