Studies of the immune response in heparin-induced thrombocytopenia
Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is caused by platelet-activating antibodies that recognize PF4/heparin complexes. Uncertainties remain regarding HIT immunobiology, including the temporal relation of antibody formation to onset of thrombocytopenia, and whether immunoglobulin class switching occurs. Using serial plasma samples from 2 heparin thromboprophylaxis trials, we determined the time of onset, antibody levels, and immunoglobulin class distributions (IgG, IgA, IgM) for 12 patients with HIT and 36 patients who formed anti-PF4/heparin antibodies, but did not develop HIT ("seropositive non-HIT controls"). In patients with HIT, anti-PF4/heparin antibodies became detectable 4 days (median) after starting heparin; antibody detection preceded the platelet count decline by 2 days (median). Patients with HIT produced higher levels of IgG antibodies, but similar IgA and IgM levels, compared with seropositive non-HIT controls. Among all 48 seroconverting patients, the first day of a positive antibody test (median, day 6) did not differ among the immunoglobulin classes. Thus, the HIT immune response does not exhibit the classic paradigm of IgM class precedence/immunoglobulin class switching; rather, relatively rapid formation of IgG antibodies is observed, sometimes with concomitant IgA and IgM formation. Compared with seropositive non-HIT controls, HIT patients develop significantly higher anti-PF4/heparin IgG levels that are detectable before the onset of thrombocytopenia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".