Heparin‐induced thrombocytopenia: pathogenesis and management – Response to Rasheed Saad
Bibliographic record
Abstract
Dr Saad correctly states that the activated partial thromboplastin time (aPTT) does not measure the anticoagulant effect of lepirudin as accurately as the ecarin clotting time (ECT). However, this advantage of ECT over aPTT is known to be relevant only in situations of very high lepirudin dosing, such as surgery requiring cardiopulmonary bypass (Warkentin & Greinacher, 2003). This is because the dose–response curve of the aPTT to increasing lepirudin concentrations flattens at high lepirudin concentrations (Greinacher, 2001). However, for most clinical situations, such as prophylaxis against or the treatment of thrombosis in acute heparin-induced thrombocytopenia (HIT), use of aPTT is believed to provide acceptable monitoring. Indeed, the pivotal clinical trials of lepirudin for the treatment of thrombosis complicating HIT used aPTT monitoring, with a target aPTT of 1.5–2.5 × baseline for most aPTT reagents (Greinacher et al, 2000). Laboratories should assess their aPTT responsiveness of assay to increasing concentrations of plasma lepirudin: if the curve begins to flatten during the high therapeutic lepirudin concentrations (about 750–1000 ng/ml), then the physician should either aim at the low end of the aPTT target range (to avoid the potential for significant overdosing that might not be apparent if the aPTT lies within the high-therapeutic aPTT range) or utilize the ECT, if available. Whether the ECT might generally provide superior anticoagulation (i.e. greater therapeutic efficacy with less bleeding) than the aPTT in non-cardiac surgery situations is unknown. This hypothesis would need testing in clinical trials comparing monitoring by aPTT versus ECT. However, even if the ECT was shown to be better, the aPTT might still remain the preferred monitoring method. Indeed, this is the current status for the monitoring of unfractionated heparin therapy in many clinical situations, although heparin levels can be measured more accurately by protamine neutralization assay or anti-factor Xa assay (Hirsh et al, 2001).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".