PB0348 Can 5B9, a Monoclonal Antibody to PF4/H, be Useful in Improving the Performance of the Asserachrom HPIA Test for the Diagnosis of HIT?
Bibliographic record
Abstract
Aims: In this study, we retrospectively evaluated the serological findings of patients referred to our platelet immunology laboratory for the diagnosis of VITT.Methods: Serological investigations of clinical samples from patients suspected of VITT following a SARS-CoV2 vaccine were retrospectively investigated.Clinical and serological findings were retrieved from patient reports.The diagnosis of VITT was based on positive enzyme-linked immunosorbent assay (ELISA) and modified heparin induced platelet aggregation assay with PF4 (mod.HIPA).An optical density (OD) ≥ 0.500 was considered reactive in ELISA.Results: A total of 301 samples from 262 patients (135 females, 127 males) were analyzed in our laboratory between March 2021 and Mai 2022.The median age of the patients was 51 years (range 14 to 87).Seventy-two samples were from our hospital and the remaining (n = 229) samples were referred from other hospital or laboratories.Anti PF4/Heparin ELISA was positive in 51 patients (19.3%) and mod.HIPA was positive in 37 patients (14%).Of these, 3 had a negative ELISA.Among ELISA+ patients, OD IgG anti-PF4/heparin was higher in mod.HIPA+ patients than mod.HIPApatients (2.44 ± 0.86 vs 1.72 ± 0.94; p = 0.009).Platelet count was available in 62 patients.Only 53% (n = 33) of these had a thrombocytopenia.Platelet count was negatively correlated with both D-dimer and IgG PF4/heparin antibody in ELISA (r = -0.33,p = 0.015 and r = -0.37,p = 0.002, respectively). Conclusion(s):The diagnosis of VITT should be based on both clinical and laboratory findings.Lack of information on submissions from external hospitals and laboratories complicates the interpretation of serological tests.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".