Heparin‐induced thrombocytopenia: An uncommon but serious complication of heparin use in renal replacement therapy
Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is an uncommon but potentially life-threatening complication of heparin therapy. Hemodialysis and hemofiltration patients are regularly exposed to heparin, which is used for extracorporeal anticoagulation. Type II HIT (HIT-II) is the rarer immune-mediated form and is of huge clinical significance. The clinical manifestation of HIT-II is characteristically with venous and arterial thrombotic events. However, systemic and pulmonary reactions have been reported. Type II HIT is due to antibodies to the heparin-platelet factor 4 complex, which induce a cascade of events leading to thrombocytopenia and thrombosis. Nowadays, with increasing availability of functional and immunoassay tests for HIT-associated antibodies, HIT diagnosis can be confirmed more readily. Hence, it is important to rapidly recognize, diagnose, and manage this syndrome early in hemodialysis patients with thrombocytopenia to avoid serious consequences resulting in morbidity and mortality. We report a case of HIT-II manifesting atypically as a "pseudopulmonary embolus" in a hemodialysis patient and discuss the clinical management of 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".