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Record W2995530109 · doi:10.1093/neuros/nyz323

Overview of Different Flow Diverters and Flow Dynamics

2019· review· en· W2995530109 on OpenAlexaff
Georgios Α. Maragkos, Adam A. Dmytriw, Mohamed M. Salem, Vincent M. Tutino, Hui Meng, Christophe Cognard, Paolo Machi, Timo Krings, Vítor Mendes Pereira

Bibliographic record

VenueNeurosurgery · 2019
Typereview
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsToronto Western HospitalUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsFlow diverterMedicinePipeline (software)Flow (mathematics)Clipping (morphology)AneurysmSurgeryComputer scienceOperating system

Abstract

fetched live from OpenAlex

Over the past decade, flow diverter technology for endocranial aneurysms has seen rapid evolution, with the development of new devices quickly outpacing the clinical evidence base. However, flow diversion has not yet been directly compared to surgical aneurysm clipping or other endovascular procedures. The oldest and most well-studied device is the Pipeline Embolization Device (PED; Medtronic), recently transitioned to the Pipeline Flex (Medtronic), which still has sparse data regarding outcomes. To date, other flow diverting devices have not been shown to outperform the PED, although information comes primarily from retrospective studies with short follow-up, which are not always comparable. Because of this lack of high-quality outcome data, no reliable recommendations can be made for choosing among flow diversion devices yet. Moreover, the decision to proceed with flow diversion should be individualized to each patient. In this work, we wish to provide a comprehensive overview of the technical specifications of all flow diverter devices currently available, accompanied by a succinct description of the evidence base surrounding each device.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.077
GPT teacher head0.314
Teacher spread0.237 · 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 teacher head, not a consensus.

Study designOther design
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

Citations50
Published2019
Admission routes1
Has abstractyes

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