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Record W4408919783 · doi:10.1161/strokeaha.124.048899

Association of Ischemic Core Hypodensity With Thrombectomy Treatment Effect in Large Core Stroke: A Secondary Analysis of the SELECT2 Randomized Controlled Trial

2025· article· en· W4408919783 on OpenAlexaffabout
Vignan Yogendrakumar, Bruce Campbell, Hannah Johns, Leonid Churilov, Felix Ng, Clark Sitton, Ameer E Hassan, Michael Abraham, Santiago Ortega‐Gutiérrez, Muzna Hussain, Michael Chen, Scott E. Kasner, Prodipta Guha, Deep Pujara, Faris Shaker, Maarten G. Lansberg, Lawrence R. Wechsler, Thanh N. Nguyen, Johanna T Fifi, Michael D. Hill, Marc Ribó, Mark Parsons, Stephen M. Davis, James C. Grotta, Gregory W. Albers, Amrou Sarraj

Bibliographic record

VenueStroke · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of CalgaryOttawa Hospital
Fundersnot available
KeywordsMedicineInterquartile rangeModified Rankin ScaleRandomized controlled trialHounsfield scaleStroke (engine)Nuclear medicineLogistic regressionComputed tomographySurgeryIschemic strokeInternal medicineIschemia

Abstract

fetched live from OpenAlex

BACKGROUND: We aimed to determine whether extensive severe computed tomography (CT) hypodensity, representing blood-brain barrier injury, would be associated with a reduced benefit of endovascular therapy (EVT) in patients presenting with large core stroke. METHODS: This study is an exploratory analysis of SELECT2 (Randomized Controlled Trial to Optimize Patient’s Selection for Endovascular Treatment in Acute Ischemic Stroke), a randomized controlled trial of EVT versus medical management in patients with large ischemic core who presented to 31 comprehensive stroke centers across the United States, Canada, Europe, Australia, and New Zealand. Visible CT hypodensity was outlined, and a threshold of severe CT hypodensity was defined as the lower 99% CI of contralateral thalamic gray matter in Hounsfield units (HU). The association between the volume of severe CT hypodensity and modified Rankin Scale (mRS) score of 0 to 3 was evaluated using logistic regression models, with adjustment for age, National Institutes of Health Stroke Scale, total noncontrast CT core volume, and a volume-by-treatment interaction. The relationship between severe CT hypodensity volume and the probability of an mRS score of 0 to 3 was used to select clinically relevant volume cut points for further evaluation. The treatment effect of EVT versus medical management on independent ambulation and hemicraniectomy was assessed in 2 subgroups based on these volume cut points. RESULTS: In 322 patients, the median CT density was 31 HU (interquartile range, 28–34). The selected threshold of severe CT hypodensity was 26 HU. The volume of ischemic core ≤26 HU (per 1 mL increase) was associated with lower odds of mRS score of 0 to 3 after EVT (adjusted odds ratio [aOR], 0.96 [95% CI, 0.94–0.99]), but not medical management (aOR, 1.01 [95% CI, 0.98–1.03]; P interaction<0.01). In 101 patients with ≥26 mL of severe CT hypodensity, EVT, compared with medical management, was not associated with mRS score of 0 to 3 (aOR, 0.98 [95% CI, 0.33–2.88]) and was associated with hemicraniectomy (≥26 mL: aOR, 3.45 [95% CI, 1.09–10.86] versus <26 mL: aOR, 0.74 [95% CI, 0.31–1.75]; P interaction=0.03), whereas among 221 patients with <26 mL of severe hypodensity EVT was associated with mRS score of 0 to 3 (aOR, 7.20 [95% CI, 3.55–15.47]; P interaction<0.01). CONCLUSIONS: Severe hypodensity within large ischemic regions modifies the thrombectomy treatment effect and increases the likelihood of hemicraniectomy, independent of lesion volume. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03876457.

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)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.262
Teacher spread0.256 · 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

Citations20
Published2025
Admission routes2
Has abstractyes

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