Anti–platelet factor 4/heparin antibodies in orthopedic surgery patients receiving antithrombotic prophylaxis with fondaparinux or enoxaparin
Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is caused by platelet-activating IgG antibodies that recognize platelet factor 4 (PF4) bound to heparin. Immunogenicity of heparins differs in that unfractionated heparin (UFH) induces more anti-PF4/heparin antibodies than low-molecular-weight heparin (LMWH) and UFH also causes more HIT. Fondaparinux, a synthetic anticoagulant modeled after the antithrombin-binding pentasaccharide, is believed to be nonimmunogenic. We tested 2726 patients for anti-PF4/heparin antibodies after they were randomized to receive antithrombotic prophylaxis with fondaparinux or LMWH (enoxaparin) following hip or knee surgery. We also evaluated in vitro cross-reactivity of the IgG antibodies generated against PF4 in the presence of UFH, LMWH, danaparoid, or fondaparinux. We found that anti-PF4/heparin antibodies were generated at similar frequencies in patients treated with fondaparinux or enoxaparin. Although antibodies reacted equally well in vitro against PF4/UFH and PF4/LMWH, and sometimes weakly against PF4/danaparoid, none reacted against PF4/fondaparinux, including even those sera obtained from patients who formed antibodies during fondaparinux treatment. At high concentrations, however, fondaparinux inhibited binding of HIT antibodies to PF4/polysaccharide, indicating that PF4/fondaparinux interactions occur. No patient developed HIT. We conclude that despite similar immunogenicity of fondaparinux and LMWH, PF4/fondaparinux, but not PF4/LMWH, is recognized poorly by the antibodies generated, suggesting that the risk of HIT with fondaparinux likely is very low.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".