Bibliographic record
Abstract
Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a highly prothrombotic disorder that like heparin-induced thrombocytopenia (HIT) is caused by platelet-activating antibodies that recognize platelet factor 4 (PF4). However, unlike HIT-where heparin at low concentrations (0.1-0.5 U/mL) typically enhances antibody-induced platelet activation, platelet activation by VITT sera is usually inhibited by heparin. Further, conventional platelet activation assays for HIT, such as the serotonin-release assay (SRA) and heparin-induced platelet activation (HIPA) test, often yield negative or atypical results when testing VITT sera. Nevertheless, VITT (like HIT) is a "clinical-pathological" disorder whereby laboratory detectability of platelet-activating anti-PF4 antibodies is crucial for diagnosis. VITT antibodies follow 2 fundamental principles of HIT laboratory testing: (1) high probability of a positive PF4-dependent enzyme-immunoassay (EIA), and (2) high probability of a positive platelet activation assay. However, optimal detection of VITT in platelet activation assays requires the addition of PF4, for example, PF4-enhanced SRA (PF4-SRA) and PF4-enhanced HIPA (PIPA). A novel whole blood assay, called the PF4-induced flow cytometry-based platelet activation (PIFPA) assay, exhibits high sensitivity and specificity for VITT. HIT and VITT sera/plasmas differ in their reactivity in rapid HIT immunoassays (90-97% sensitivity for HIT, <25% sensitivity for VITT), consistent with distinct antigen sites on PF4 recognized by HIT and VITT antibodies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".