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Record W3087376334 · doi:10.1161/strokeaha.120.030620

Challenging the Ischemic Core Concept in Acute Ischemic Stroke Imaging

2020· review· en· W3087376334 on OpenAlexaff
Mayank Goyal, Johanna M. Ospel, Bijoy K. Menon, Mohammed Almekhlafi, Mahesh Jayaraman, Jens Fiehler, Marios Psychogios, René Chapot, Aad van der Lugt, Jianmin Liu, Pengfei Yang, Ronit Agid, Werner Hacke, Melanie Walker, Urs Fischer, Negar Asdaghi, Ryan McTaggart, Padma MV Srivastava, Raul G. Nogueira, J. Moret, Jeffrey L. Saver, Michael D. Hill, Diederik W.J. Dippel, Marc Fisher

Bibliographic record

VenueStroke · 2020
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsMedicineStroke (engine)Magnetic resonance imagingAcute strokeGold standard (test)Intensive care medicineCore (optical fiber)TerminologyNeuroimagingCLARITYThrombolysisRadiologyCardiologyInternal medicineMyocardial infarctionTissue plasminogen activator

Abstract

fetched live from OpenAlex

Endovascular treatment is a highly effective therapy for acute ischemic stroke due to large vessel occlusion and has recently revolutionized stroke care. Oftentimes, ischemic core extent on baseline imaging is used to determine endovascular treatment-eligibility. There are, however, 3 fundamental issues with the core concept: First, computed tomography and magnetic resonance imaging, which are mostly used in the acute stroke setting, are not able to precisely determine whether and to what extent brain tissue is infarcted (core) or still viable, due to variability in tissue vulnerability, the phenomenon of selective neuronal loss and lack of a reliable gold standard. Second, treatment decision-making in acute stroke is multifactorial, and as such, the relative importance of single variables, including imaging factors, is reduced. Third, there are often discrepancies between core volume and clinical outcome. This review will address the uncertainty in terminology and proposes a direction towards more clarity. This theoretical exercise needs empirical data that clarify the definitions further and prove its value.

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 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 score0.794
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.035
GPT teacher head0.320
Teacher spread0.285 · 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.

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

Citations215
Published2020
Admission routes1
Has abstractyes

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