MétaCan
Menu
Back to cohort

Ultrashort imaging to reperfusion time interval arrests core expansion in endovascular therapy for acute ischemic stroke

2012· article· en· W2038422976 on OpenAlexafffundabout
Mohammed Almekhlafi, Muneer Eesa, Bijoy K. Menon, Andrew M. Demchuk, Mayank Goyal

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsFoothills Medical CentreHotchkiss Brain InstituteUniversity of Calgary
FundersUniversity of Calgary
KeywordsMedicineIschemic strokeStroke (engine)CardiologyCore (optical fiber)Acute strokeReperfusion therapyInternal medicineIschemiaTissue plasminogen activator

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: The shorter the time interval between the estimation of the ischemic core by imaging and reperfusion, the more likely that core expansion is minimized. We aimed to assess the feasibility of achieving an ultrashort imaging to reperfusion time in routine clinical practice. METHODS: The study subjects were a prospective cohort of patients with acute ischemic stroke treated with endovascular therapy in a tertiary center in whom an imaging to reperfusion time of <60 min was achieved. RESULTS: Imaging to reperfusion time of <60 min was accomplished in 11 patients. The median baseline National Institutes of Health Stroke Scale (NIHSS) score was 11 and the median baseline Alberta Stroke Program Early CT Score (ASPECTS) was 8. The median time interval from imaging to endovascular reperfusion was 47 min. The median ASPECTS score on the 24 h CT scan was also 8 and the median 24 h NIHSS score was 1. Upon discharge, 82% of patients achieved a modified Rankin scale score of ≤ 1. CONCLUSIONS: An imaging to endovascular reperfusion time of <60 min is feasible and resulted in minimal core expansion on follow-up imaging in patients with 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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.505
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.031
GPT teacher head0.309
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

Citations26
Published2012
Admission routes3
Has abstractyes

Explore more

Same venueJournal of NeuroInterventional SurgerySame topicAcute Ischemic Stroke ManagementFrench-language works237,207