Antithrombotics in intracerebral hemorrhage in the era of novel agents and antidotes: A review
Bibliographic record
Abstract
BACKGROUND: Intracerebral hemorrhage (ICH)1 is characterized by the pathological accumulation of blood within the brain parenchyma, most commonly associated with hypertension, arteriovenous malformations, or trauma. However, it can also present in patients receiving antithrombotic drugs, either anticoagulants such as acenocoumarol/warfarin-novel oral anticoagulants or antiplatelets, for the prevention and treatment of thromboembolic disease. OBJECTIVE: The purpose of this review is to present current bibliographic data regarding ICH irrespective of the cause, as well as post-hemorrhage use of antithrombotic agents. Moreover, this review attempts to provide guidelines concerning the termination, inversion, and of course resumption of antithrombotic therapy. METHODS AND MATERIALS: We reviewed the most recently presented available data for patients who dealt with intracerebral hemorrhagic events while on antithrombotic agents (due to atrial fibrillation, prosthetic mechanical valves or recent/recurrent deep vein thrombosis). Furthermore, we examined and compared the thromboembolic risk, the bleeding risk, as well as the re-bleeding risk in two groups: patients receiving antithrombotic therapy versus patients not on antithrombotic therapy. CONCLUSION: Antithrombotic therapy is of great importance when indicated, though it does not come without crucial side-effects, such as ICH. Optimal timing of withdrawal, reversal, and resumption of antithrombotic treatment should be determined by a multidisciplinary team consisting of a stroke specialist, a cardiologist, and a neurosurgeon, who will individually approach the needs and risks of each patient.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".