Platelet Count Monitoring and Laboratory Testing for Heparin-Induced Thrombocytopenia
Bibliographic record
Abstract
OBJECTIVE: Heparin-induced thrombocytopenia (HIT) is an antibody-mediated adverse drug reaction that paradoxically is associated with a brief but dramatically increased risk for thrombosis (transient acquired thrombophilia). The objective of this article is to provide practical recommendations for platelet count monitoring in patients receiving heparin, as well as for selection of laboratory assays to detect pathogenic HIT antibodies. STUDY SELECTION: Relevant literature that focused on frequency and timing of HIT in various clinical settings and that dealt with laboratory testing for HIT antibodies was critically appraised. DATA EXTRACTION AND SYNTHESIS: The author prepared a preliminary manuscript including recommendations that was presented to participants at the College of American Pathologists Conference XXXVI: Diagnostic Issues in Thrombophilia (November 10, 2001). Support of at least 70% of conference participants was required for recommendations to be adopted. CONCLUSIONS: The risk of immune HIT varies depending on the type of heparin (unfractionated heparin greater than low-molecular-weight heparin) and patient population (surgical greater than medical). Thus, the intensity of platelet count monitoring should be stratified depending on the clinical situation. Platelet count monitoring should focus on the period of highest risk (usually days 5 to 10 after starting heparin) and should use an appropriate platelet count baseline (generally, the highest platelet count beginning 4 days after start of heparin). However, earlier platelet count monitoring is appropriate if the patient received heparin within the past 100 days, as already circulating HIT antibodies can cause rapid-onset HIT with heparin reexposure. Although both antigen and (washed platelet) activation assays are very sensitive for detecting clinically significant HIT antibodies, activation assays have greater diagnostic specificity for clinical HIT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".