Pitfalls in the diagnosis of heparin‐Induced thrombocytopenia: A 6‐year experience from a reference laboratory
Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is caused by platelet-activating antibodies against complexes of platelet factor 4 (PF4) and heparin. The diagnosis of HIT is contingent on accurate and timely laboratory testing. Recently, alternative anticoagulants for the treatment of HIT have been introduced along with algorithms for better HIT diagnosis. However, the increased reliance on immunoassays for the diagnosis of HIT may have harmful consequences due to the high rate of false positive results. To compare trends and implications of current HIT testing approaches, we analyzed results over a six-year period from the McMaster University Platelet Immunology Reference Laboratory. From 2008 to 2013, 8,546 samples were investigated for HIT using both an in-house IgG-specific anti-PF4/heparin enzyme immunoassay (EIA) and the serotonin-release assay (SRA). Of 8,546 samples tested, 13.4% were true-positives (positive in both assays); 65.6% were true-negatives (negative in both assays); 20.9% were presumed false positive for HIT (EIA-positive/SRA-negative); and 0.2% were EIA-negative/SRA-positive. The frequency of EIA-positive/SRA-negative results increased over time (from 12.9% in 2008 to 22.9% in 2013). We found that the number of SRA-negative samples was reduced from referring centers that used an immunoassay as an initial screen; however, 41% of those samples tested negative in the immunoassay and in the SRA at the reference laboratory. The suspicion of HIT continues at a high rate and the agreement between the EIA and SRA test results remains problematic.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".