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Record W3131301666 · doi:10.1161/str.48.suppl_1.wp40

Abstract WP40: Quick CT Perfusion Evaluation of Leptomeningeal Collateral Circulation: Single Cortical CBV-ROI

2017· article· en· W3131301666 on OpenAlexaboutno aff
Marta Rubiera, Alvaro Garcia-Tornell, Sandra Boned, Nicolás Romero, Pilar Coscojuela, Marián Muchada, Jesús Juega, Noelia Rodríguez‐Villatoro, David Rodríguez‐Luna, Jorge Pagola, Carlos A. Molina, Marc Ribó, Alejandro Tomasello

Bibliographic record

VenueStroke · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCollateral circulationPerfusionStroke (engine)OcclusionNuclear medicinePerfusion scanningRegion of interestRadiologyAngiographyMiddle cerebral arteryIschemiaInternal medicine

Abstract

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Good collateral circulation (CC) is a strong outcome predictor in acute stroke patients. CT angiography (CTA) is wide-world available but does not provide accurate information about parenchymal status. CT perfusion (CTP) is frequently used to determine ischemic core and tissue at risk. Our aim was to identify an easy and quick method to evaluate CC status by CTP. Methods: Consecutive ischemic stroke patients <8h from symptoms onset evaluated for reperfusion therapies were studied. Non-contrast CT, CTP and multiphase CTA were performed. Patients with confirmed M1-MCA or TICA occlusion on CTA were included. CC evaluation was determined by multiphase CTA (mCTA) according to the Calgary CC Scale and classified as poor (grades 0-2) or good (grades 3-5). In CTP maps, one single ipsi- and contralateral regions of interest (ROI) were defined in the MCA cortical territory (M4, M5, M6). We studied the association of absolute and relative to contralateral ROI-CTP values with CC degree determined by mCTA. Results: 33 patients were included, median NIHSS 17.5 (2-22). Twenty-five patients (75.8%) presented a M1 and 8 (24.2%) a TICA occlusion. On mCTA, 27 (81.8%) patients presented with a favourable CC status and 6 (18.2%) with poor CC. Mean ROI values in the ischemic MCA territory were: CBV 3.5±1.5 ml/100mg, CBF 46.9±29.3 ml/100mg/min, MTT 8.1±3.1 s, Tmax 23.2±4.4 s. In the contralateral non-ischemic MCA, the mean ROI values were: CBV 3.48±1.4, CBF 66.5±32.7, MTT 5.6±2.3, Tmax 20.4±4.8. Absolute and relative CBV-ROI data (relCBV= ischemic CBV value / contralateral CBV value) were the only values significantly associated with CC status on mCTA (good CC mean CBV: 3.8 ml/100g VS poor CC mean CBV: 1.9, p=0.006; good CC mean relCBV 1.1 vs poor CC mean relCBV 0.6, p=0.019). A ROC curve defined 2.5 ml/100mg as the better cut-off point of ROI-CBV that identified patients with good CC status (sensitivity 96%, specificity 84%, VPP 0.96, VPN 0.83). Patients with a ROI-CBV >2.5 presented lower median NIHSS after 24 hours (4 vs 18, p= 0.012) and smaller mean infarct volume on control CT (27.9 vs 88.3, p=0.021). Conclusion: A single cortical ROI-CBV allows an easy and quick accurate evaluation of collateral circulation in CTP. ROI-CBV>2.5 ml/100mg is related to good clinical and radiological outcomes.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.053
GPT teacher head0.333
Teacher spread0.280 · 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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Citations0
Published2017
Admission routes1
Has abstractyes

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