Bibliographic record
Abstract
PURPOSE OF REVIEW: Thrombocytopenia and heparin exposure are common in critically ill patients, yet immune heparin-induced thrombocytopenia (HIT), a prothrombotic adverse effect of heparin, rarely accounts for thrombocytopenia in this patient population. The review discusses the clinical and laboratory features that distinguish HIT from non-HIT thrombocytopenia. RECENT FINDINGS: The frequency of HIT in heparin-exposed critically ill patients is approximately 0.3-0.5% versus at least a 30-50% background frequency of non-HIT thrombocytopenia. Most patients who form anti-PF4/heparin antibodies do not develop HIT, contributing to HIT overdiagnosis. Disseminated intravascular coagulation (DIC), particularly in the setting of cardiogenic or septic shock associated with 'shock liver', can cause ischemic limb gangrene with pulses, mimicking a clinical picture of HIT. However, whereas non-HIT-related DIC with microthrombosis can be treated with heparin, HIT usually requires nonheparin anticoagulation. HIT-associated DIC can result in an elevated INR, which could reflect factor VII depletion because of extrinsic (tissue factor) pathway-mediated activation of coagulation. SUMMARY: Greater understanding of the various clinical and laboratory features that distinguish HIT from non-HIT thrombocytopenia could help improve outcomes in patients who develop thrombocytopenia and coagulopathies in the ICU.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".