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Record W4394723354 · doi:10.3389/fneur.2024.1272890

Application of imaging modalities for endovascular thrombectomy of large core infarcts in clinical practice

2024· article· en· W4394723354 on OpenAlexaboutno aff
Hai Zeng, Qingfeng Zhu

Bibliographic record

VenueFrontiers in Neurology · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineModalitiesModality (human–computer interaction)RadiologyCore (optical fiber)Stroke (engine)Perfusion scanningMagnetic resonance imagingClinical trialMedical physicsPerfusionComputer scienceInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

Four randomized controlled trials of large infarct core volume (LICV) included three imaging modalities: non-contrast CT (NCCT)-Alberta Stroke Program Early CT Score (ASPECTS), diffusion-weighted imaging (DWI)-ASPECTS, and NCCT-ASPECTS combined with CTP (CT perfusion). However, there is no clear consensus on the optimal imaging modality for endovascular thrombectomy (EVT) trials of large core infarcts. The variety and complexity of imaging modalities make it difficult to apply them in clinical practice. By familiarizing ourselves with these imaging modalities, we can better apply them in the clinic and correctly screen patients with large core infarcts in the anterior circulation who can benefit from EVT therapy.

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)0.006
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.344
Teacher spread0.327 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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