MétaCan
Menu
Back to cohort

Impact of immediate post-reperfusion cooling on outcome in patients with acute stroke and substantial ischemic changes

2016· article· en· W2306275148 on OpenAlexaboutno aff
Yang‐Ha Hwang, Ji-Su Jeon, Yong-Won Kim, Dong‐Hun Kang, Yong-Sun Kim, David S. Liebeskind

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)CardiologyIschemic strokeInternal medicineIschemia

Abstract

fetched live from OpenAlex

BACKGROUND: In patients with acute stroke and an extensive ischemic burden at baseline, the prognosis is usually poor despite timely reperfusion. OBJECTIVE: To overcome universally poor outcomes in such patients, by applying immediate 'post-reperfusion cooling' in order to reduce reperfusion-related complications, and to describe the clinical and imaging characteristics. METHODS: Patients having (1) an acute anterior large vessel occlusive stroke within 4.5 h since last known well, (2) Alberta Stroke Program Early CT Score (ASPECTS) ≤5 on baseline imaging, and (3) targeted temperature management with endovascular cooling after confirmed reperfusion were included in this study. RESULTS: Eighteen patients (mean±SD age 59.5±10.9 years, median National Institutes of Health Stroke Scale score of 17, and median ASPECTS of 3) were analyzed. Median lesion volumes at baseline and after treatment were 130.2 and 110.6 mL, respectively. Median time from onset to the start of hypothermia and hypothermia duration were 213 min and 51 h, respectively. Favorable outcome (modified Rankin Scale ≤2) at 3 months was observed in 10 (55.6%) patients. Symptomatic intracranial hemorrhage, malignant brain edema, and pneumonia were observed in 2, 6, and 8 patients, respectively. CONCLUSIONS: The use of post-reperfusion cooling as a rescue treatment in patients with substantial ischemia at baseline might improve clinical outcome.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.278

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 head0.023
GPT teacher head0.300
Teacher spread0.277 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of NeuroInterventional SurgerySame topicThermal Regulation in MedicineFrench-language works237,207