Managing thrombosis risk in flow diversion: A review of antiplatelet approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it