Managing thrombosis risk in flow diversion: A review of antiplatelet approaches
Bibliographic record
Abstract
Flow diversion is a transformative approach in neurointerventional surgery for intracranial aneurysms that relies heavily on effective antiplatelet therapy. The ideal approach, including the timing of treatment, the use of dual antiplatelet therapy (DAPT), and the number of flow-diverter devices to use, remains unknown. DAPT, which combines aspirin with a thienopyridine like clopidogrel, prasugrel, or ticagrelor, is the standard regimen, balancing thromboembolic protection and hemorrhagic risk. The variable response to clopidogrel, influenced by genetic polymorphisms, necessitates personalized treatment strategies. Alternatives like prasugrel and ticagrelor provide superior efficacy in specific scenarios but require careful consideration of bleeding risks and costs. Platelet function testing plays a critical role in tailoring antiplatelet regimens for patients undergoing flow diversion for intracranial aneurysms. Special considerations were made for ruptured aneurysms, and the implications of the extensive metallic surface of flow diverters on platelet activation were noted. Emerging technologies such as drug-eluting flow diverters and reversal agents for P2Y12 inhibitors suggest a potential shift toward more refined antiplatelet strategies in the future. Personalized medication that is compatible with the stent structure and metal is essential for optimizing patient outcomes in cerebral flow diversion procedures. Ongoing research and multidisciplinary collaboration will be key in refining these strategies and enhancing the safety and efficacy of neurointerventional treatments.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".