Bibliographic record
Abstract
Many critically ill patients receive heparin, either before intensive care unit (ICU) admission (e.g., postcardiac surgery), for prophylaxis/treatment of thrombosis, for hemodialysis/filtration, or even incidentally (e.g., flushing of intravascular catheters), and are therefore at risk for developing immune heparin-induced thrombocytopenia (HIT), a prothrombotic drug reaction caused by platelet-activating antiplatelet factor 4 (PF4)/heparin antibodies. However, HIT explains at most 1 in 100 thrombocytopenic ICU patients (HIT frequency 0.3-0.5% vs. 30-50% background frequency of ICU-associated thrombocytopenia), and most patients who form anti-PF4/heparin antibodies do not develop HIT; hence, HIT overdiagnosis often occurs. This review discusses HIT-related issues relevant to ICU patients, including how to (1) distinguish HIT both clinically and serologically from non-HIT-related thrombocytopenia; (2) recognize HIT-mimicking disorders, such as the acute disseminated intravascular coagulation (DIC)/liver necrosis-limb necrosis syndrome; (3) prevent HIT in the ICU through use of low-molecular-weight heparin; and (4) treat HIT, including awareness of "PTT confounding" when anticoagulating patients with DIC.
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 | 0.001 |
| 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 | 0.000 |
| 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".