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Record W4400893343 · doi:10.1136/jnis-2024-snis.170

E-065 Flow diversion for pediatric intracranial aneurysms: preliminary clinical and radiological data from a pediatric tertiary university hospital

2024· article· en· W4400893343 on OpenAlexaff
C Parra-Farinas, S. Chowdhury, Vanessa Rea, Suzanne Bickford, Jeffrey Quon, Abhaya V. Kulkarni, Vítor Mendes Pereira, Julian Spears, Thomas R. Marotta, Leonardo R. Brandão, P Dirks, Prakash Muthusami

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsSt. Michael's HospitalSickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsRadiological weaponMedicineTertiary careRadiologyGeneral surgery

Abstract

fetched live from OpenAlex

Introduction and Purpose Flow diversion is a well-established endovascular therapy for treating intracranial aneurysms in adults. Although most pediatric intracranial aneurysms are effectively treated by deconstructive techniques, sometimes this is not feasible or safe. We aim to share our experience in treating complex intracranial aneurysms with flow-diverting stents. Materials and Methods We conducted a retrospective analysis of consecutive patients aged 17 or younger who underwent treatment with flow diversion for intracranial aneurysms at a pediatric tertiary university center between March 2018 and May 2023. Demographic, clinical, and radiological characteristics at presentation and long-term follow-up were collected and analyzed. Pre-procedural and post-procedural antiplatelet medication administration was also documented. Safety was defined as ischemic/hemorrhagic events and mortality, while efficacy was measured by complete occlusion at final follow-up. Angiographic occlusion was assessed according to the O’Kelly-Marotta classification. Results Five patients (4 females, 1 male) were included with a mean age at treatment of 11.2 years (range: 6–15). One patient had sickle cell disease and another had a suprasellar adenoma. One patient presented with subarachnoid hemorrhage, while one each presented with third nerve palsy, recurrent transient ischemic attacks and interval growth, and one was iatrogenic secondary to a neurosurgical procedure. Four patients failed balloon occlusion tests and no test was performed in one patient. Aneurysms were located in the internal carotid artery (n=3), middle cerebral artery (n=1), and posterior cerebral artery (n=1). Aneurysm types included saccular (n=2), traumatic/pseudoaneurysm (n=1), dissecting/dysplastic (n=1), and blister-like (n=1). All patients received weight-based dual antiplatelet premedication: aspirin and clopidogrel (n=2), aspirin and ticagrelor (n=1), and aspirin and eptifibatide (n=2); platelet testing was not available. A total of nine flow diverters were deployed, including Pipeline Flex (n=3), Pipeline Vantage (n=4), Silk Vista Baby (n=1), and Surpass Evolve (n=1). Optical coherence tomography was used in one case to acutely evaluate the stent apposition. Two patients underwent adjuvant coiling. One patient needed angioplasty after stent deployment. There were no immediate intraprocedural complications. Two patients developed symptomatic ischemic complications at 48 hours and 3 months post-treatment and one patient had an asymptomatic infarct on 24 hours post-procedure MRI. Dual antiplatelet medication was maintained for 6 months (n=3) and 12 months (n=2) based on imaging features and prior thrombotic events. The last follow-up imaging, including CT/MR Angiography and/or catheter angiography (mean: 17.6 months; range: 7–30) revealed complete occlusion of all aneurysms. One patient developed asymptomatic complete occlusion of the flow diverter construct with interval development of extensive pial collateralization 12 months after treatment. Conclusion Initial short-term observations indicate that flow diversion is a feasible technique for well-selected pediatric intracranial aneurysms that cannot be effectively deconstructed. However, the risk of ischemic complications is significant, and extended monitoring is essential to better understand potential long-term risks and complications. Disclosures C. Parra-Farinas: None. S. Chowdhury: None. V. Rea: None. S. Bickford: None. J. Quon: None. A. Kulkarni: None. V. Pereira: None. J. Spears: None. T. Marotta: None. L. Brandao: None. P. Dirks: None. P. Muthusami: None.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.036
GPT teacher head0.291
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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