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Record W2890040036 · doi:10.1097/rct.0000000000000783

Evaluating the Prognosis of Ischemic Stroke Using Low-Dose Multimodal Computed Tomography Parameters in Hyperacute Phase

2018· article· en· W2890040036 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Computer Assisted Tomography · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineConfidence intervalModified Rankin ScaleComputed tomography angiographyCerebral blood flowOdds ratioStroke (engine)Perfusion scanningRadiologyAngiographyNuclear medicinePerfusionIschemiaIschemic strokeInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study was to evaluate the potential value of low-dose multimodal computed tomography (CT) in predicting prognosis of acute ischemic stroke (AIS) within 6 hours. METHODS: The admission "one-stop-shop" multimodal CT examination, including noncontrast CT (NCCT), low-dose CT perfusion, and CT angiography (CTA), was performed in patients with symptoms of stroke within 6 hours. Noncontrast CT, CTA source image (CTA-SI), cerebral blood flow (CBF), cerebral blood volume (CBV), time to peak (TTP), and mean transit time (MTT) maps were studied using Alberta Stroke Program Early CT Score (ASPECTS). The regional leptomeningeal collateral (rLMC) score (0-20) was dichotomized into 2 groups: good (11-20) and poor (0-10) rLMC. Poor functional outcomes were defined by a modified Rankin scale score of 3 to 6. RESULTS: One hundred forty-four patients were ultimately selected; 43.8% of them showed poor functional outcomes. They had lower ASPECTSs on NCCT, CTA-SI, CBV, CBF, TTP, and MTT, and poor rLMC was more frequently associated with poor functional outcomes (all P < 0.001). In the multivariate analysis for AIS patients with conservative treatment, CTA-SI-ASPECTS 6 or less (odds ratio [OR], 5.9; 95% confidence interval [95% CI], 1.9-18.4; P = 0.002) and poor collaterals (OR, 5.0; 95% CI, 1.3-15.4; P = 0.017), CBV-ASPECTS 6 or less (OR, 8.0; 95% CI, 2.7-24.0; P < 0.001), CBF-ASPECTS 4 or less (OR, 8.0; 95% CI, 2.0-31.5; P = 0.003), MTT-ASPECTS≤3 (OR, 5.8; 95% CI, 1.8-18.1; P = 0.003), TTP-ASPECTS 4 or less (OR, 5.0; 95% CI, 1.6-15.1; P = 0.005), and NCCT-ASPECTS 8 or less (OR, 5.9; 95% CI, 1.7-20.4; P = 0.005) were significantly associated with poor functional outcome. In the multivariate analysis for AIS patients with thrombolysis, CTA-SI-ASPECTS 6 or less (OR, 27.5; 95% CI, 2.9-262.3; P = 0.004), poor collaterals (OR, 28.0; 95% CI, 2.8-283.0; P < 0.028), and CBV-ASPECTS 6 or less (OR, 18.0; 95% CI, 3.0-107.7; P = 0.002) were associated with poor functional outcomes. Furthermore, the area under the curve (AUC) of the combination of CTA-SI-ASPECTS 6 or less, poor collaterals, and CBV-ASPECTS 6 or less (AUC, 0.87) was greater than that for any single parameter alone: CTA-SI-ASPECTS 6 or less (AUC, 0.80; P < 0.001), poor collaterals (AUC, 0.76; P < 0.001), and CBV-ASPECTS 6 or less (AUC, 0.81; P = 0.002). CONCLUSIONS: The combination of CTA-SI-ASPECTS, collaterals, and CBV-ASPECTS may improve predictive power compared with a single parameter alone.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.354
Teacher spread0.302 · 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