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Record W2169125170 · doi:10.1002/jmri.21763

Evolution of hyperacute stroke over 6 hours using serial MR perfusion and diffusion maps

2009· article· en· W2169125170 on OpenAlexafffund
Ashley D. Harris, Robert K. Kosior, Henry Chen, Richard Frayne

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2009
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsHotchkiss Brain InstituteFoothills Medical CentreUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlberta Heritage Foundation for Medical ResearchFondation pour la Recherche Médicale
KeywordsPerfusionStroke (engine)MedicineInfarctionPerfusion scanningVoxelHomogeneousEffective diffusion coefficientDiffusion MRIAcute strokeCardiologyCerebral blood flowNuclear medicineRadiologyInternal medicineMagnetic resonance imagingMyocardial infarctionMathematicsTissue plasminogen activator

Abstract

fetched live from OpenAlex

PURPOSE: To develop an appropriate method to evaluate the time-course of diffusion and perfusion changes in a clinically relevant animal model of ischemic stroke and to examine lesion progression on MR images. An exploration of acute stroke infarct expansion was performed in this study by using a new methodology for developing time-to-infarct maps based on the time at which each voxel becomes infarcted. This enabled definition of homogeneous regions from the heterogeneous stroke infarct. MATERIALS AND METHODS: Time-to-infarct maps were developed based on apparent diffusion coefficient (ADC) changes. These maps were validated and then applied to blood flow and time-to-peak maps to examine perfusion changes. RESULTS: ADC stroke infarct showed different evolution patterns depending on the time at which that region of tissue infarcted. Applying the time-to-infarct maps to the perfusion maps showed localized perfusion evolution characteristics. In some regions, perfusion was immediately affected and showed little change over the experiment; however, in some regions perfusion changes were more dynamic. CONCLUSION: Results were consistent with the diffusion-perfusion mismatch hypothesis. In addition, characteristics of collateral recruitment were identified, which has interesting stroke pathophysiology and treatment implications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.287
Teacher spread0.278 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations18
Published2009
Admission routes2
Has abstractyes

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