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Record W2755974114 · doi:10.1055/s-0037-1604299

New Treatment Approaches to Arteriovenous Malformations

2017· review· en· W2755974114 on OpenAlexaff
Patrick Gilbert, Josée Dubois, M.F. Giroux, Gilles Soulez

Bibliographic record

VenueSeminars in Interventional Radiology · 2017
Typereview
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineArteriovenous malformationRadiologyIntensive care medicine

Abstract

fetched live from OpenAlex

Arteriovenous malformations (AVMs) are high-flow vascular anomalies that have demonstrated a very high recurrence rate after endovascular treatment, surgical treatment, or a combination of both. Surgical treatments have shown good response when they are small and well localized but a poor response when diffuse. A better understanding of the nature of the lesion has led to a better response rate and a safer treatment for these patients. This has been accomplished through a detailed understanding of the angioarchitecture of the lesion, enabling a tailored approach in reaching and targeting the nidus of the AVM with different liquid embolic agents, more specifically ethanol. Flow reduction techniques help in exposing the nidus to sclerosant agents. A clinical classification, the Schobinger classification, will help determine the appropriate time to start or to pursue therapy.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.343
GPT teacher head0.412
Teacher spread0.069 · 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

Citations73
Published2017
Admission routes1
Has abstractyes

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