Bibliographic record
Abstract
Heparin-induced thrombocytopenia, or HIT, can present in many ways, ranging from common-isolated thrombocytopenia, venous thromboembolism, acute limb ischemia-to less common but specific presentations-necrotizing skin lesions at heparin injection sites, post-bolus acute systemic reactions, and adrenal hemorrhagic necrosis (secondary to adrenal vein thrombosis). Many patients with HIT have mild or moderate thrombocytopenia: the median platelet count nadir is 60 x 10(9)/L, and ranges from 15 to 150 x 10(9)/L in 90% of patients, most of whom evince a 50% or greater fall in the platelet count. HIT that begins after stopping heparin ("delayed-onset HIT") is increasingly recognized. Factors influencing risk of HIT include type of heparin (unfractionated heparin > low-molecular-weight heparin), type of patient (surgical > medical), and gender (female > male). Since timely diagnosis and treatment of HIT may reduce the risk of adverse outcomes, this review focuses on those clinical circumstances that should prompt the clinician to "think of HIT." Coumarin anticoagulants such as warfarin are ineffective in acute HIT and can even be deleterious by predisposing to micro-thrombosis via protein C depletion (venous limb gangrene and skin necrosis syndromes). Thus, it is important to avoid or postpone coumarin while managing HIT hypercoagulability, focusing on agents that inhibit thrombin directly (lepirudin, argatroban) or that inhibit its generation (danaparoid, fondaparinux). Post-marketing experience suggests that standard dosing of lepirudin is too high; current recommendations are to avoid the initial lepirudin bolus and to begin with lower infusion rates, even in patients without overt renal dysfunction.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.045 | 0.018 |
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".