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Record W2156266161 · doi:10.1148/radiol.13112310

Intracranial Vasa Vasorum: Insights and Implications for Imaging

2013· review· en· W2156266161 on OpenAlexaff
Anthony Portanova, Niloofar Hakakian, David J. Mikulis, Renu Virmani, Wael Abdalla, Bruce A. Wasserman

Bibliographic record

VenueRadiology · 2013
Typereview
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVasa vasorumMedicineVasculitisRadiologyPathologyDisease

Abstract

fetched live from OpenAlex

This review offers insight into the possible role of vasa vasorum in the development of intracranial vascular disease. Unique structural features of intracranial vessels and nourishment from the surrounding cerebrospinal fluid environment may account for the relative lack of intracranial vasa vasorum early in life. However, with advancing age and with the development of vascular disease, vasa vasorum are known to develop. Advanced contrast material-enhanced imaging techniques are capable of helping detect and even grade intracranial vasa vasorum, and this may provide new insights into our ability to diagnose and assess the risk of intracranial vascular lesions such as atherosclerosis, aneurysms, dissections, and vasculitis.

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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.028
GPT teacher head0.324
Teacher spread0.296 · 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

Citations188
Published2013
Admission routes1
Has abstractyes

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