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Record W2978234314 · doi:10.1161/strokeaha.119.026229

Admission Diffusion-Weighted Imaging Lesion Volume in Patients With Large Vessel Occlusion Stroke and Alberta Stroke Program Early CT Score of ≥6 Points

2019· article· en· W2978234314 on OpenAlexaffabout
Inwu Yu, Oh Young Bang, Jong‐Won Chung, Yoon‐Chul Kim, Eun‐Hyeok Choi, Woo‐Keun Seo, Gyeong‐Moon Kim, Bijoy K. Menon, Andrew M. Demchuk, Mayank Goyal, Michael D. Hill

Bibliographic record

VenueStroke · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineStroke (engine)OcclusionLesionDiffusion MRIRadiologyCardiologyNuclear medicineMagnetic resonance imagingSurgery

Abstract

fetched live from OpenAlex

Background and Purpose— We hypothesized that the pial collateral status at the time of presentation could predict the infarct size on magnetic resonance imaging in patients with similar degrees of early ischemic changes on computed tomography. We tested the association between serial changes in collateral status and infarct volume defined on diffusion-weighted imaging (DWI) in patients with large vessel occlusion and small core. Methods— Consecutive patients who were candidates for endovascular treatment (Alberta Stroke Program Early CT Score [ASPECTS] of ≥6 points) and who underwent both pretreatment multiphasic computed tomography angiography (mCTA) and multimodal magnetic resonance imaging were enrolled. The baseline early ischemic changes and collateral status were determined using both mCTA and magnetic resonance imaging–based collateral maps. Multivariable linear regression was used to evaluate adjusted estimates of the effect of collateral status on predicting MR DWI lesion volume before endovascular treatment. Results— Of 65 patients (39 men; median age, 76 years; median ASPECTS, 8 points [range, 6–10]), 10 (15.4%), 8 (12.3%), and 47 (72.3%) presented poor, intermediate, and good collaterals on mCTA, respectively. After adjusting for the initial stroke severity, ASPECTS, time to DWI, and mismatch volume, the mCTA collateral grade was the only factor independently associated with the DWI lesion volume (β=−35.657, SE mean=3.539; P <0.0001). An excellent correlation between the mCTA- and magnetic resonance imaging-based collateral grades was observed (matching grade seen in 92.3%), suggesting a collateral status persistence during the hyperacute stroke phase. Conclusions— The mCTA assessed collateral adequacy is the sole predictor of eventual DWI lesion volume before endovascular treatment. The added value of collateral assessment in early ischemic changes and large vessel occlusion for decision-making regarding more aggressive revascularizations requires further evaluation. Clinical Trial Registration— URL: https://www.clinicaltrials.gov . Unique identifier: NCT03234634 and NCT02668627.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.230
Teacher spread0.225 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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