What is the potential for overdiagnosis of heparin‐induced thrombocytopenia?
Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is caused by platelet-activating antibodies that recognize platelet factor 4//heparin (PF4/H) complexes. According to the "iceberg model," only a subset of anti-PF4/heparin antibodies of IgG class evincing strong platelet-activating properties cause clinical HIT. Since many centers rely predominantly on an anti-PF4/polyanion enzyme-immunoassay (EIA) to diagnose HIT, we estimated the potential for overdiagnosis when only this single test is available. We examined a database of 100 patients in whom the probability of HIT had been estimated using a clinical scoring system (4Ts), and where patients underwent systematic testing for HIT antibodies using three assays: the platelet serotonin release assay (SRA), an "in-house" EIA that detects IgG anti-PF4/heparin antibodies (EIA-IgG), and a commercial EIA that detects anti-PF4/polyanion antibodies of all three immunoglobulin classes (EIA-GTI). Whereas 16 of 100 patients fulfilled a "classic" definition of HIT (intermediate/high probability plus strong platelet-activating anti-PF4/heparin IgG antibodies), an additional 16 patients fulfilled a "liberal" definition in which any investigated patient (irrespective of the pretest probability) who had a positive EIA-GTI was considered to have HIT. The clinical features of these 16 additional patients--including generally weak antibodies and low risk for thrombosis--suggest underlying non-HIT explanations for thrombocytopenia. Patients with a positive SRA generally corresponded to those with intermediate or high pretest probability of HIT who also had strong EIA-GTI reactivity (>1.20 OD units). We conclude there is the potential to overdiagnose HIT by approximately 100% if any positive EIA is considered to "confirm" the diagnosis of HIT irrespective of the clinical scenario.
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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 | 0.081 | 0.374 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".