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Record W2804246295 · doi:10.1161/strokeaha.117.019863

Clinical Utility of Electronic Alberta Stroke Program Early Computed Tomography Score Software in the ENCHANTED Trial Database

2018· article· en· W2804246295 on OpenAlexaboutno aff
Simon Nagel, Xia Wang, Cheryl Carcel, Thompson Robinson, Richard I. Lindley, John Chalmers, Craig S. Anderson

Bibliographic record

VenueStroke · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersNational Institutes of HealthGeorge Institute for Global HealthServierNational Institute for Health and Care Research
KeywordsMedicineModified Rankin ScaleThrombolysisInterquartile rangeStroke (engine)Odds ratioConfidence intervalLogistic regressionRandomized controlled trialPhysical therapyInternal medicineIschemic strokeMyocardial infarctionIschemia

Abstract

fetched live from OpenAlex

Background and Purpose— Clinical utility of electronic Alberta Stroke Program Early CT Score (e-ASPECTS), an automated system for quantifying signs of infarction, was evaluated in a large database of thrombolyzed patients with acute ischemic stroke. Methods— All baseline noncontrast computed tomographic scans of patients with anterior circulation acute ischemic stroke who participated in the alteplase dose arm of the randomized controlled trial ENCHANTED (Enhanced Control of Hypertension and Thrombolysis Stroke Study) were reviewed; poor quality and large (>6 mm) slice thickness were excluded. Included scans had e-ASPECTS scores correlated with baseline neurological severity (National Institutes of Health Stroke Scale scores) and 90-day disability outcomes (modified Rankin Scale scores). Multivariable logistic regression models were used to determine the predictive ability of e-ASPECTS for disability outcomes and symptomatic intracranial hemorrhage. Results— Of 2426 available computed tomographic images, 1480 (61%) were included in analyses of e-ASPECTS scores (median 9 [interquartile range, 8–10], 77% with good [range, 8–10] scores). Lower e-ASPECTS scores (per 1-point decrease) were significantly associated with increasing baseline National Institutes of Health Stroke Scale scores ( r , −0.31; P <0.0001) and 90-day poor outcome (modified Rankin Scale scores, 2–6; r , −0.27; P <0.001). Adjusted odds ratios and 95% confidence intervals for 90-day outcomes were death or disability (modified Rankin Scale scores, 2–6; 0.91 [0.85–0.97]), death and major disability (modified Rankin Scale scores, 3–6; 0.89 [0.83–0.95]), and death (0.86 [0.79–0.95]); and for symptomatic intracranial hemorrhage, according to the Implementation of Thrombolysis in Stroke-Monitoring Study criteria was 0.87 (0.72–1.05). Conclusions— e-ASPECT scores from thin computed tomographic slices (≤6 mm) were highly correlated with baseline neurological severity and independently predict functional recovery and adverse outcomes in acute ischemic stroke. Clinical Trial Registration— URL: https://www.clinicaltrials.gov . Unique identifier: NCT01422616.

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)0.007
metaresearch head score (Gemma)0.023
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 score0.997
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.337
Teacher spread0.301 · 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".

Quick stats

Citations39
Published2018
Admission routes1
Has abstractyes

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