The clinical and laboratory diagnosis of vaccine-induced immune thrombotic thrombocytopenia
Bibliographic record
Abstract
Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare but serious adverse syndrome occurring 5 to 30 days after adenoviral vector COVID-19 vaccination. Therefore, a practical evaluation of clinical assessments and laboratory testing for VITT is needed to prevent significant adverse outcomes as the global use of adenoviral vector vaccines continues. We received the clinical information and blood samples of 156 patients in Canada with a suspected diagnosis of VITT between April and July 2021. The performance characteristics of various diagnostic laboratory tests were evaluated against the platelet factor 4 (PF4)-14C-serotonin release assay (SRA) including a commercial anti-PF4/heparin immunoglobulin G (IgG)/IgA/IgM enzyme immunoassay (EIA, PF4 Enhanced; Immucor), in-house IgG-specific anti-PF4 and anti-PF4/heparin-EIAs, the standard SRA, and the PF4/heparin-SRA. Of those, 43 (27.6%) had serologically confirmed VITT-positive based on a positive PF4-SRA result and 113 (72.4%) were VITT-negative. The commercial anti-PF4/heparin EIA, the in-house anti-PF4-EIA, and anti-PF4/heparin-EIA were positive for all 43 VITT-confirmed samples (100% sensitivity) with a few false-positive results (mean specificity, 95.6%). These immunoassays had specificities of 95.6% (95% confidence interval [CI], 90.0-98.6), 96.5% (95% CI, 91.2-99.0), and 97.4% (95% CI, 92.4-99.5), respectively. Functional tests, including the standard SRA and PF4/heparin-SRA, had high specificities (100%), but poor sensitivities for VITT (16.7% [95% CI, 7.0-31.4]; and 46.2% [95% CI, 26.6-66.6], respectively). These findings suggest EIA assays that can directly detect antibodies to PF4 or PF4/heparin have excellent performance characteristics and may be useful as a diagnostic test if the F4-SRA is unavailable.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".