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Record W2394628912 · doi:10.1097/rmr.0000000000000083

Intracranial Vessel Wall MRI

2016· review· en· W2394628912 on OpenAlexaff
Joanna D. Schaafsma, David J. Mikulis, Daniel M. Mandell

Bibliographic record

VenueTopics in Magnetic Resonance Imaging · 2016
Typereview
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsMagnetic resonance imagingLumen (anatomy)MedicineArterial wallRadiologySubarachnoid hemorrhagePathologicalStroke (engine)PathologySurgeryCardiologyPhysics

Abstract

fetched live from OpenAlex

Intracranial vessel wall magnetic resonance imaging (VW-MRI) can be a useful diagnostic technique in patients with ischemic stroke and subarachnoid hemorrhage. Unlike conventional vascular imaging that depicts only the vessel lumen, VW-MRI allows visualization of pathology in the arterial wall itself. The ability to image the arterial wall is useful, as many pathological processes reside within the wall and only secondarily affect the lumen. In this review, we will present 6 clinical uses for intracranial wall imaging to highlight the versatility of this 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.023
GPT teacher head0.310
Teacher spread0.288 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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