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Record W4407216522 · doi:10.1136/jnnp-2024-334974

Efficacy and safety of preoperative embolization in surgical treatment of brain arteriovenous malformations: a multicentre study with propensity score matching

2025· article· en· W4407216522 on OpenAlexaff
Hamza Salim, Dawoud Hamdan, Nimer Adeeb, Sandeep Kandregula, Assala Aslan, Basel Musmar, Christopher S. Ogilvy, Adam A. Dmytriw, Ahmed Abdelsalam, Cagdas Ataoglu, Ufuk Erginoğlu, Douglas Kondziolka, Kareem El Naamani, Jason P. Sheehan, Natasha Ironside, Deepak Kumbhare, Sanjeev Gummadi, Muhammed Amir Essibayi, Salem M Tos, Abdullah Keleş, Sandeep Muram, Daniel Sconzo, Arwin Rezai, Omar Alwakaa, Johannes Pöppe, Rajeev Sen, Mustafa K. Başkaya, Christoph J. Griessenauer, Pascal Jabbour, Stavropoula Tjoumakaris, Elias Atallah, Howard A. Riina, Abdallah Abushehab, Christian Swaid, Jan‐Karl Burkhardt, Robert M. Starke, Laligam N. Sekhar, Michael R. Levitt, David Altschul, Neil Haranhalli, Malia McAvoy, Adib A. Abla, Christopher J. Stapleton, Matthew J. Koch, Visish M. Srinivasan, Peng Roc Chen, Spiros Blackburn, Joseph Cochran, Omar Choudhri, Bryan Pukenas, Darren B. Orbach, Edward R. Smith, Markus Moehlenbruch, Pascal J. Mosimann, Ali Alaraj, Mohammad Ali Aziz‐Sultan, Aman B. Patel, Vivek Yedavalli, Max Wintermark, Amey Savardekar, Hugo H Cuellar, Michael T. Lawton, Jacques J. Morcos, Bharat Guthikonda

Bibliographic record

VenueJournal of Neurology Neurosurgery & Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineEmbolizationMicrosurgeryPropensity score matchingModified Rankin ScaleSurgeryComplicationArteriovenous malformationInternal medicineIschemic stroke

Abstract

fetched live from OpenAlex

BACKGROUND: Brain arteriovenous malformations (AVMs) are abnormal connections between feeding arteries and draining veins, associated with significant risks of haemorrhage, seizures and other neurological deficits. Preoperative embolization is commonly used as an adjunct to microsurgical resection, with the aim of reducing intraoperative complications and improving outcomes. However, the efficacy and safety of this approach remain controversial. METHODS: This study is a subanalysis of the Multicenter International Study for Treatment of Brain AVMs consortium. We retrospectively analysed 486 patients with brain AVMs treated with microsurgical resection between January 2010 and December 2023. Patients were divided into two groups: those who underwent microsurgery alone (n=245) and those who received preoperative embolization, followed by microsurgery (n=241). Propensity score matching was employed, resulting in 288 matched patients (144 in each group). The primary outcomes were rates of complete AVM obliteration and functional outcomes (measured by the modified Rankin Scale (mRS)). Secondary outcomes included complication rates, mortality, hospital length of stay and postsurgical rupture. RESULTS: After matching, the complete obliteration rate was 97% with no significant difference between the microsurgery-only group and the preoperative embolization group (p=0.12). The proportion of patients with an mRS score of 0-2 at the last follow-up was similar in both groups (83% vs 84%; p=0.67). The median hospital stay was significantly longer for the embolisation group (9 days vs 7 days; p=0.017). Complication rates (24% vs 22%; p=0.57) and mortality rates (4.9% vs 2.1%; p=0.20) were comparable between the two groups. No significant differences were observed in postsurgical rupture, recurrence or retreatment rates. CONCLUSIONS: In this large multicentre study, preoperative embolization did not significantly improve AVM obliteration rates, functional outcomes or reduce complications compared with microsurgery alone.

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)0.008
metaresearch head score (Gemma)0.021
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 score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.275
Teacher spread0.258 · 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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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