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Record W2616056347 · doi:10.1097/rmr.0000000000000129

CT-based Techniques for Brain Perfusion

2017· review· en· W2616056347 on OpenAlex
Pradeep Krishnan, Amanda Murphy, Richard I. Aviv

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

VenueTopics in Magnetic Resonance Imaging · 2017
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSunnybrook Health Science CentreHealth Sciences Centre
Fundersnot available
KeywordsMedicineTriageNeuroimagingStroke (engine)Acute strokePerfusion scanningRadiologyPerfusionMagnetic resonance imagingCerebral perfusion pressureIntensive care medicineInternal medicineTissue plasminogen activatorEmergency medicine

Abstract

fetched live from OpenAlex

Recent rapid advances in endovascular treatment for acute ischemic stroke highlight the crucial role of neuroimaging especially multimodal computed tomography (CT) including CT perfusion in stroke triage and management decisions. With an increasing focus on changes in cerebral physiology along with time-based matrices in clinical decisions for acute ischemic stroke, CT perfusion provides a rapid and practical modality for assessment and identification of salvageable tissue at risk and infarct core and provides a better understanding of the changes in cerebral physiology. Although there are challenges with the lack of standardization and accuracy of quantitative assessment, CT perfusion is evolving as a cornerstone for imaging-based strategies in the rapid management of acute ischemic stroke.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.049
GPT teacher head0.378
Teacher spread0.329 · 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