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Record W3000104626 · doi:10.1002/ana.25669

Optimizing Patient Selection for Endovascular Treatment in Acute Ischemic Stroke (SELECT): A Prospective, Multicenter Cohort Study of Imaging Selection

2020· article· en· W3000104626 on OpenAlexaboutno aff
Amrou Sarraj, Ameer E Hassan, James C. Grotta, Clark Sitton, Gary Cutter, Chunyan Cai, Peng Roc Chen, Bita Imam, Deep Pujara, Ashish Arora, Sujan Reddy, Kaushik Parsha, Roy Riascos, Nirav Vora, Michael Abraham, Randall C. Edgell, Frank R Hellinger, Diogo C Haussen, Spiros Blackburn, Haris Kamal, Andrew D. Barreto, Sheryl Martin‐Schild, Maarten G. Lansberg, Rishi Gupta, Sean I. Savitz, Gregory W. Albers

Bibliographic record

VenueAnnals of Neurology · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProspective cohort studyConfidence intervalOdds ratioConcordancePerfusion scanningRadiologyStroke (engine)Computed tomography angiographyCohortAngiographyNeuroimagingInternal medicinePerfusion

Abstract

fetched live from OpenAlex

OBJECTIVE: The primary imaging modalities used to select patients for endovascular thrombectomy (EVT) are noncontrast computed tomography (CT) and CT perfusion (CTP). However, their relative utility is uncertain. We prospectively assessed CT and CTP concordance/discordance and correlated the imaging profiles on both with EVT treatment decisions and clinical outcomes. METHODS: A phase 2, multicenter, prospective cohort study of large-vessel occlusions presented up to 24 hours from last known well was conducted. Patients received a unified prespecified imaging evaluation (CT, CT angiography, and CTP with Rapid Processing of Perfusion and Diffusion software mismatch determination). The treatment decision, EVT versus medical management, was nonrandomized and at the treating physicians' discretion. An independent, blinded, neuroimaging core laboratory adjudicated favorable profiles based on predefined criteria (CT:Alberta Stroke Program Early CT Score ≥ 6, CTP:regional cerebral blood flow (<30%) < 70ml with mismatch ratio ≥ 1.2 and mismatch volume ≥ 10ml). RESULTS: Of 4,722 patients screened from January 2016 to February 2018, 361 patients were included. Two hundred eighty-five (79%) received EVT, of whom 87.0% had favorable CTs, 91% favorable CTPs, 81% both favorable profiles, 16% discordant, and 3% both unfavorable. Favorable profiles on the 2 modalities correlated similarly with 90-day functional independence rates (favorable CT = 56% vs favorable CTP = 57%, adjusted odds ratio [aOR] = 1.91, 95% confidence interval [CI] = 0.40-9.01, p = 0.41). Having a favorable profile on both modalities significantly increased the odds of receiving thrombectomy as compared to discordant profiles (aOR = 3.97, 95% CI = 1.97-8.01, p < 0.001). Fifty-eight percent of the patients with favorable profiles on both modalities achieved functional independence as compared to 38% in discordant profiles and 0% when both were unfavorable (p < 0.001 for trend). In favorable CT/unfavorable CTP profiles, EVT was associated with high symptomatic intracranial hemorrhage (sICH) (24%) and mortality (53%) rates. INTERPRETATION: Patients with favorable imaging profiles on both modalities had higher odds of receiving EVT and high functional independence rates. Patients with discordant profiles achieved reasonable functional independence rates, but those with an unfavorable CTP had higher adverse outcomes. Ann Neurol 2020;87:419-433.

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)0.002
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.293
Teacher spread0.270 · 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".

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Citations68
Published2020
Admission routes1
Has abstractyes

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