Combination of two complementary automated rapid assays for diagnosis of heparin‐induced thrombocytopenia (HIT)
Bibliographic record
Abstract
BACKGROUND: HIT diagnosis typically uses complementary diagnostic assays (eg, a PF4-dependent enzyme-immunoassay [EIA] and a platelet activation assay such as the serotonin-release assay [SRA]). OBJECTIVES: To determine whether the combination of two automated assays-a latex immunoturbidimetric assay (LIA) that evaluates competitive inhibition of a HIT-like monoclonal antibody and a chemiluminescence immunoassay (CLIA) for detecting anti-PF4/heparin IgG-optimizes diagnostic sensitivity while also yielding good specificity, particularly at high assay reactivities. PATIENTS/METHODS: We determined operating characteristics using combined LIA/CLIA results from a HIT observational trial (n = 430; derivation cohort) and 147 consecutive patients with HIT (n = 147; supplementary derivation cohort). We also evaluated 678 consecutive samples referred for HIT testing (replication cohort). LIA/CLIA reactivities were scored individually as "negative" (<1.00 U/mL, 0 points), "weak" (1.00-4.99 U/mL, 1 point), "moderate" (5.00-15.99 U/mL, 2 points) and "strong" (≥16.00 U/mL, 3 points), thus contributing up to 6 points (maximum) when LIA/CLIA results were combined. We also examined whether higher LIA/CLIA scores predicted presence of platelet-activating antibodies by conventional and modified (PF4- or PF4/heparin-enhanced) SRA. RESULTS: Combined LIA/CLIA testing yielded high diagnostic sensitivity (~99%) similar to EIA. Interpretation of LIA/CLIA results using the 6-point scale indicated progressively greater likelihood for the presence of platelet-activating antibodies with increasing scores (semi-quantitative reactivity). A LIA/CLIA score ≥ 4 points predicted the presence of platelet-activating antibodies by SRA or PF4-enhanced SRA with high probability (~98%). CONCLUSION: Combined LIA/CLIA testing optimizes diagnostic sensitivity, with progressively greater probability of detecting platelet-activating antibodies with higher assay reactivity that reaches 98% when both automated assays yield moderate or strong results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".