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Record W4395020789 · doi:10.1161/strokeaha.124.046725

Most Promising Approaches to Improve Brain AVM Management: ARISE I Consensus Recommendations

2024· article· en· W4395020789 on OpenAlexaff
Edgar A. Samaniego, Guilherme Dabus, Philip M. Meyers, Peter Kan, Juhana Frösén, Giuseppe Lanzino, Babu G. Welch, Victor Volovici, L. Fernando Gonzalez, Johanna T Fifi, Fady T. Charbel, Brian L. Hoh, Alexander A. Khalessi, Michael P. Marks, Alejandro Berenstein, V Mendes Pereira, Mark Bain, Geoffrey P. Colby, Sandra Narayanan, Satoshi Tateshima, Adnan H. Siddiqui, Ajay K. Wakhloo, Adam S Arthur, Michael T. Lawton

Bibliographic record

VenueStroke · 2024
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsSt. Michael's Hospital
FundersNational Institutes of HealthTampereen YliopistoStrykerAmerican Heart Association
KeywordsMedicineRadiosurgeryIntracerebral hemorrhageMicrosurgeryArteriovenous malformationStroke (engine)Medical physicsIntensive care medicineSurgerySubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

Brain arteriovenous malformations (bAVMs) are complex, and rare arteriovenous shunts that present with a wide range of signs and symptoms, with intracerebral hemorrhage being the most severe. Despite prior societal position statements, there is no consensus on the management of these lesions. ARISE (Aneurysm/bAVM/cSDH Roundtable Discussion With Industry and Stroke Experts) was convened to discuss evidence-based approaches and enhance our understanding of these complex lesions. ARISE identified the need to develop scales to predict the risk of rupture of bAVMs, and the use of common data elements to perform prospective registries and clinical studies. Additionally, the group underscored the need for comprehensive patient management with specialized centers with expertise in cranial and spinal microsurgery, neurological endovascular surgery, and stereotactic radiosurgery. The collection of prospective multicenter data and gross specimens was deemed essential for improving bAVM characterization, genetic evaluation, and phenotyping. Finally, bAVMs should be managed within a multidisciplinary framework, with clinical studies and research conducted collaboratively across multiple centers, harnessing the collective expertise and centralization of resources.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.061
GPT teacher head0.288
Teacher spread0.227 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

Quick stats

Citations34
Published2024
Admission routes1
Has abstractyes

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