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Record W4376638554 · doi:10.1136/jnis-2023-020393

Transvenous retrograde pressure cooker technique for embolization of an ethmoidal dural arteriovenous fistula

2023· article· en· W4376638554 on OpenAlexaff
How‐Chung Cheng, Vivek Bodani, Emily Chung, Pascal J. Mosimann

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMedicineArteriovenous fistulaEmbolizationCookerSurgeryFistulaRadiology

Abstract

fetched live from OpenAlex

SummaryEthmoidal dural arteriovenous fistulas (DAVFs) are rare lesions, accounting for approximately 10% of intracranial DAVFs.1 2 As ethmoidal DAVFs commonly demonstrate cortical venous drainage, treatment is always warranted.2-6 Endovascular transvenous embolization has been increasingly reported as an effective and safe treatment for ethmoidal DAVFs, and since occlusion of the central retinal artery and resulting blindness are not a concern, it has an advantage over transarterial embolization.3-6 To ensure curative embolization, we adopted the transvenous retrograde pressure cooker technique (RPCT), creating a plug with n-butyl cyanoacrylate (NBCA) in the draining vein to allow a more comprehensive and efficient injection of Onyx (Medtronic, MN) while avoiding excessive reflux.7 8 In this technical video (video 1), we report the first case using the transvenous RPCT for successful Onyx embolization of an ethmoidal DAVF, with emphasis on the technical nuances of the RPCT and important tips to avoid periprocedural complications. neurintsurg;16/7/742/V1F1V1Video 1 Video demonstrating Onyx embolization of an ethmoidal dural arteriovenous fistula using the transvenous retrograde pressure cooker technique.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.038
GPT teacher head0.309
Teacher spread0.270 · 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 designCase report
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

Citations5
Published2023
Admission routes1
Has abstractyes

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