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Record W2589460953 · doi:10.1177/1971400917690166

Susceptibility weighted imaging in acute cerebral ischemia: review of emerging technical concepts and clinical applications

2017· review· en· W2589460953 on OpenAlex
Charlie Hsu, Gigi Nga Chi Kwan, Sachintha Hapugoda, Michelle Craigie, Trevor Watkins, E. Mark Haacke

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Neuroradiology Journal · 2017
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSusceptibility weighted imagingMedicineStroke (engine)Magnetic resonance imagingIschemic strokeAcute strokeNeuroimagingIschemiaBrain ischemiaPerfusion scanningRadiologyCardiologyInternal medicinePerfusion

Abstract

fetched live from OpenAlex

Susceptibility weighted imaging (SWI) is an essential magnetic resonance imaging sequence in the assessment of acute ischemic stroke. In this article, we discuss the physics principals and clinical application of conventional SWI and multi-echo SWI sequences. We review the research evidence and practical approach of SWI in acute ischemic stroke by focusing on the detection and characterization of thromboembolism in the cerebral circulation. In addition, we discuss the role of SWI in the assessment of neuroparenchyma by depiction of asymmetric hypointense cortical veins in the ischemic territory (surrogate tissue perfusion), detection of existing microbleeds before stroke treatment and monitoring for hemorrhagic transformation post-treatment. In conclusion, the SWI sequence complements other parameters in the stroke magnetic resonance imaging protocol and understanding of the research evidence is vital for practising stroke neurologists and neuroradiologists.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
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 head0.066
GPT teacher head0.454
Teacher spread0.388 · 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