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Record W3128398440 · doi:10.1155/2021/5935170

Predictive Value of the Alberta Stroke Program Early CT Score (ASPECTS) in the Outcome of the Acute Ischemic Stroke and Its Correlation with Stroke Subtypes, NIHSS, and Cognitive Impairment

2021· article· en· W3128398440 on OpenAlexaboutno aff
Ahmed Esmael, Mohammed Elsherief, Khaled Eltoukhy

Bibliographic record

VenueStroke Research and Treatment · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLogistic regressionStroke (engine)CutoffCorrelationInternal medicineInfarctionReceiver operating characteristicCardiologyLacunar strokeAcute strokePhysical therapyIschemic strokeIschemiaMyocardial infarction

Abstract

fetched live from OpenAlex

Objectives. This study is aimed at correlating ASPECTS with mortality and morbidity in patients with acute middle cerebral artery territory infarction and at determining the cutoff value of ASPECTS that may predict the outcome. Methods. 150 patients diagnosed with acute middle cerebral artery territory infarction were involved in this study. Risk factors, initial NIHSS, and GCS were determined. An initial or follow-up noncontrast CT brain was done and assessed by ASPECTS. Outcomes were determined by mRS during the follow-up of cases after 3 months. Correlations of ASPECTS and outcome variables were done by Spearman correlation. Logistic regression analysis and ROC curve were done to detect the cutoff value of ASPECTS that predicts unfavorable outcomes. Results. The most common subtypes of ischemic strokes were lacunar stroke in 66 patients (44%), cardioembolic stroke in 39 patients (26%), and LAA stroke in 30 cases (20%). The cardioembolic stroke had a statistically significant lower ASPECT score than other types of ischemic strokes ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>P</a:mi> <a:mo>&lt;</a:mo> <a:mn>0.05</a:mn> </a:math> ). Spearman correlation showed that lower ASPECTS values (worse outcome) were more in older patients and associated with lower initial GCS. ASPECTS values were inversely correlated with initial NIHSS, inpatient stay, inpatient complications, mortality, and mRS. The ASPECTS cutoff value determined for the prediction of unfavorable outcomes was equal to ≤7. The binary logistic regression analysis detected that patients with <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mtext>ASPECTS</c:mtext> <c:mo>≤</c:mo> <c:mn>7</c:mn> </c:math> were significantly associated with about fourfold increased risk of poor outcomes (OR 3.95, 95% CI 2.09–11.38, and <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>P</e:mi> <e:mo>&lt;</e:mo> <e:mn>0.01</e:mn> </e:math> ). Conclusions. ASPECTS is a valuable and appropriate technique for the evaluation of the prognosis in acute ischemic stroke. Patients with high ASPECTS values are more likely to attain favorable outcomes, and the cutoff value of ASPECTS is a strong predictor for unfavorable outcomes. This trial is registered with ClinicalTrials.gov NCT04235920.

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 score0.051
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.318
Teacher spread0.291 · 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

Citations35
Published2021
Admission routes1
Has abstractyes

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