High-dose intravenous immunoglobulin G and usual heparin anticoagulation for urgent cardiac surgery in a patient with severe autoimmune heparin-induced thrombocytopenia
Bibliographic record
Abstract
A 56-year-old woman required urgent cardiac surgery for Streptococcus mitis mitral valve infective endocarditis complicated by severe autoimmune heparin-induced thrombocytopenia (aHIT). We reasoned that the combination of high-dose intravenous immunoglobulin G (IVIG; to mitigate aHIT antibody-mediated platelet activation in the presence of heparin) together with the high concentrations of heparin attained during cardiac surgery (which typically produces less platelet activation in vitro vs usual therapeutic heparin concentrations) might prove effective. Accordingly, our patient underwent cardiac surgery with heparin following high-dose IVIG (1 g/kg × 2) without intra- or postoperative thrombosis. Serial serotonin release assays, using blood obtained pre-/post-IVIG, showed minimal platelet activation (∼30% serotonin release) post-IVIG at heparin concentrations typically obtained during cardiac surgery (2-5 U/mL) and significantly less than pre-IVIG serum in heparin's absence (∼85% serotonin release). In the setting of urgent cardiac surgery, preoperative high-dose IVIG appears to be a reasonable strategy to reduce platelet-activating effects of heparin-induced thrombocytopenia (including aHIT) antibodies, permitting safe use of standard intraoperative heparin dosing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".