Platelet function inhibitors and platelet function testing in neurointerventional procedures
Bibliographic record
Abstract
Over the past decade there has been a growing use of intracranial stents for the treatment of both ischemic and hemorrhagic cerebrovascular disease, including stents to assist in the remodeling of the neck of aneurysms as well as the use of flow diverting devices for aneurysm treatment. With this increase in stent usage has come a growing need for the neurointerventional (NI) community to understand the pharmacology of medications used for modifying platelet function, as well as the testing methodologies available. Platelet function testing in NI procedures remains controversial. While pre-procedural antiplatelet assays might lead to a reduced rate of thromboembolic complications, little evidence exists to support this as a standard of care practice. Despite the routine use of dual antiplatelet therapy (DAT) with aspirin and a P2Y12 receptor antagonist (such as clopidogrel, prasugrel, or ticagrelor) in most neuroembolization procedures necessitating intraluminal reconstruction devices, thromboembolic complications are still encountered.1–3 Moreover, DAT carries the risk of hemorrhagic complications, with intracerebral hemorrhage (ICH) being the most potentially devastating.4 ,5 Light transmission aggregometry (LTA) is the gold standard to test for platelet reactivity, but it is usually expensive and may not be easily obtainable at many centers. This has led to the development of point-of-care assays, such as the VerifyNow (Accumetrics, San Diego, California, USA), which correlates strongly with LTA and can reliably measure the degree of P2Y12 receptor inhibition.6–9 VerifyNow results are reported in P2Y12 reaction units (PRUs), with a lower PRU value corresponding to a higher level of P2Y12 receptor inhibition and, presumably, a lower probability of platelet aggregation, and a higher PRU value corresponding to a lower level of P2Y12 receptor inhibition and, hence, a higher chance of platelet activation and aggregation. While aspirin resistance is perhaps less common, clopidogrel resistance may be more challenging as …
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".