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Record W2938350860 · doi:10.1002/ana.25484

Flow diversion for treatment of intracranial aneurysms: Mechanism and implications

2019· review· en· W2938350860 on OpenAlexaff
Melissa Chua, Luke Silveira, Justin M. Moore, Vítor Mendes Pereira, Ajith J. Thomas, Adam A. Dmytriw

Bibliographic record

VenueAnnals of Neurology · 2019
Typereview
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFlow diverterAneurysmMedicineFlow (mathematics)Mechanism (biology)Blood flowOcclusionRadiologyCardiologyMechanicsPhysics

Abstract

fetched live from OpenAlex

Flow diverters are new generation stents that have recently garnered a large amount of interest for use in treatment of intracranial aneurysms. Flow diverters reduce blood flow into the aneurysm, with redirection along the path of the parent vessel. Flow stagnation into the aneurysm and neck coverage with subsequent endothelialization are the important synergistic mechanisms by which the therapy acts. Several studies have examined the mechanisms by which flow diverters subsequently lead to aneurysm occlusion. This review aims to provide a general overview of the flow diverters and their mechanism of action and potential implications. ANN NEUROL 2019;85:793-800.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.142
GPT teacher head0.380
Teacher spread0.238 · 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

Citations38
Published2019
Admission routes1
Has abstractyes

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