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Record W2990886275 · doi:10.4103/ajns.ajns_234_19

Expert consensus on the management of brain arteriovenous malformations

2019· review· en· W2990886275 on OpenAlexaff
Yoko Kato, Van Dong, Feres Chaddad-Neto, Katsumi Takizawa, Tsuyoshi Izumo, Hitoshi Fukuda, Takayuki Hara, Ken‐ichiro Kikuta, Yasunobu Nakai, Toshiki Endo, Hiroki Kurita, Bin Xu, Vladimír Beneš, Christian Raftopoulos, Giacomo Pavesi, Mojgan Hodaie, Rajan Sharma, Harshal Agarwal, Krishna Mohan, B S Liew

Bibliographic record

VenueAsian Journal of Neurosurgery · 2019
Typereview
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineRadiosurgeryIntracranial Arteriovenous MalformationsArteriovenous malformationEmbolizationConservative managementModalitiesRadiologySurgeryCerebral angiographyAngiographyRadiation therapy

Abstract

fetched live from OpenAlex

Brain arteriovenous malformations (bAVMs) are complex, heterogeneous, and uncommon intracranial lesions. They can be treated by one or a combination of the following treatment modalities, namely embolization, radiosurgery, or microsurgical resection. In Spetzler-Martin Grade 4 and 5 arteriovenous malformations (AVMs), conservative management may be the best option. A group of experts in the management of AVMs of different disciplines gathered in January 2019 in Hanoi to compile the "Expert Consensus on the Management of Brain Arteriovenous Malformations".

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.003

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 head0.091
GPT teacher head0.337
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations61
Published2019
Admission routes1
Has abstractyes

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