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Predictors and clinical impact of infarct progression rate in the ESCAPE-NA1 trial

2021· article· en· W3198507548 on OpenAlexafffundabout
Johanna M. Ospel, Rosalie McDonough, Andrew M. Demchuk, Bijoy K. Menon, Mohammed Almekhlafi, Raul G. Nogueira, Ryan McTaggart, Alexandre Y. Poppe, Brian Buck, Daniel Roy, Diogo C Haussen, René Chapot, Thalia S. Field, Mahesh Jayaraman, Michael Tymianski, Michael D. Hill, Mayank Goyal

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryCentre Hospitalier de l’Université de MontréalNoNO (Canada)Université de MontréalUniversity of AlbertaFoothills Medical Centre
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsMedicineInternal medicineCardiologyClinical trial

Abstract

fetched live from OpenAlex

BACKGROUND: Determining infarct progression rate in acute ischemic stroke (AIS) is important for patient triage, treatment decision-making, and outcome prognostication. OBJECTIVE: To estimate infarct progression rate in patients with AIS with large vessel occlusion (LVO) and determine its predictors and impact on clinical outcome. METHODS: Data are from the ESCAPE-NA1 Trial. Patients with AIS with time from last known well to randomization <6 hours and near-complete reperfusion following endovascular treatment were included. Infarct growth rate (mL/h) was estimated by dividing 24 hour infarct volume (measured by non-contrast CT or diffusion-weighted magnetic resonance imaging) by time from last known well to reperfusion. Multivariable linear regression was used to assess the association of patient baseline variables with log-transformed infarct progression rate. The association of infarct progression rate and good outcome (modified Rankin Scale score 0-2) was determined using multivariable logistic regression. RESULTS: Four hundred and nine patients were included in the study. Median infarct progression rate was 4.74 mL/h (IQR 1.25-14.84). Collateral status (β: -0.81 (95% CI -1.20 to -0.41)), Alberta Stroke Program Early CT Score (ASPECTS, β: -0.34 (95% CI -0.46 to -0.23)), blood glucose(β: 0.09 (95% CI 0.02 to 0.16)), and National Institutes of Health Stroke Scale (NIHS score (β: 0.07 (95% CI 0.04 to 0.10)) were associated with log-transformed infarct progression rate. Clinical and imaging baseline variables explained 23% of the variance in infarct progression rate. Infarct progression rate was significantly associated with good outcome (aOR per 1 mL/h increase: 0.96 (95% CI 0.95 to 0.98)). CONCLUSION: In this sample of patients presenting within the early time window with LVO and near-complete recanalization, infarct progression rate was significantly associated with good outcome. A significant association between ASPECTS, collateral status, blood glucose, and NIHSS score was observed, but baseline imaging and clinical characteristics explained only a small proportion of the interindividual variance. More research on measurable factors affecting infarct growth is needed.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.052
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.059
GPT teacher head0.389
Teacher spread0.330 · 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

Citations17
Published2021
Admission routes3
Has abstractyes

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