Performance characteristics of an automated latex immunoturbidimetric assay [HemosIL ® HIT-Ab (PF4-H) ] for the diagnosis of immune heparin-induced thrombocytopenia
Bibliographic record
Abstract
BACKGROUND: Heparin-induced thrombocytopenia (HIT) is a prothrombotic drug reaction caused by platelet-activating anti-PF4/heparin antibodies. Given time-sensitive treatment considerations, a rapid and accurate laboratory test for HIT antibodies is needed. AIMS: , a rapid, on-demand, fully-automated, latex immunoturbidimetric assay (LIA), for diagnosis of HIT. METHODS: We evaluated LIA sensitivity, specificity, negative (NPV) and positive predictive value (PPV), negative (LR-) and positive likelihood ratio (LR+), using citrated-plasma from 429 patients (prospective cohort study of 4Ts scoring; HIT, n=31), and from consecutive HIT patients (n=125), using reference standard serotonin-release assay (SRA). Comparators included two PF4-dependent enzyme-immunoassays (EIAs). We used stratum-specific likelihood ratios (SSLRs) to determine how differing magnitudes of LIA-positivity influenced post-test probability of HIT. RESULTS: LIA operating characteristics were: sensitivity=97.4% (152/156); specificity=94.0% (374/398); PPV=55.6% (30/54); and NPV=99.7% (374/375). At manufacturers' cutoffs, LIA specificity and PPV were superior to the EIAs. Although a negative LIA pointed strongly against HIT (LR-, 0.034), the post-test probability was ~2% with high 4Ts score. The LIA's LR+ was high (16.0), with SSLRs rising substantially with greater LIA-positivity: 5.7 (1.0-4.9U/mL), 31 (5.0-15.9U/mL), and 128 (≥16U/mL). A LIA-positive result (at 1.0 cutoff) indicated at least 24% HIT probability (low 4Ts score), rising to 90% with high 4Ts score. CONCLUSIONS: Although approximately 1 in 40 SRA-positive patients tested LIA-negative, the LIA's high NPV and PPV indicate that this rapid assay is useful for the diagnostic evaluation of HIT, including in low pre-test situations.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".