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Record W4408207739 · doi:10.1186/s12883-025-04111-w

Early venous filling is associated with unfavorable outcomes in acute ischemic stroke with large vessel occlusion after mechanical thrombectomy: a real-world analysis

2025· article· en· W4408207739 on OpenAlexaboutno aff
Jiaxin Han, Yixuan Wu, Zihan Wang, Jianfeng Han, Guogang Luo, Kang Huo

Bibliographic record

VenueBMC Neurology · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOdds ratioLogistic regressionInternal medicineStroke (engine)ConfoundingNeurologyUnivariate analysisMultivariate analysisNeurosurgeryRetrospective cohort studySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The presence of early venous filling (EVF) post-mechanical thrombectomy (MT) in acute ischemic stroke (AIS) patients has been observed, yet its prognostic value for clinical outcomes remains underexplored. This study aimed to assess the correlation between EVF and poor clinical outcomes in AIS patients who underwent MT. MATERIALS AND METHODS: This retrospective analysis included AIS patients with large vessel occlusions treated with MT at the First Affiliated Hospital of Xi'an Jiaotong University from January 2018 to June 2023. The primary outcome was mRS at 90 days, secondary outcomes included hemorrhagic transformation, symptomatic intracranial hemorrhage, and malignant brain edema. The study used inverse probability weighting for balancing baseline characteristics and employed univariate and multivariate logistic regression analyses to explore the association between EVF and clinical outcomes. G*Power was used to calculate the sample size. RESULTS: Among 307 patients, 75 (24.4%) presented with EVF. Patients with EVF had significantly higher rates of unfavorable outcomes at 90 days (76.00% vs. 46.12%, P < 0.001). Multivariate analysis revealed significant associations between EVF and unfavorable outcome (odds ratio [OR] = 2.69, 95%CI [1.37-5.26], P = 0.004), hemorrhagic transformation (OR = 3.11, 95%CI [1.73-5.62], P < 0.001), symptomatic intracranial hemorrhage (OR = 3.24, 95%CI 1.42 to 7.37, P = 0.005), and malignant brain edema (OR = 3.06, 95%CI [1.56-6.01], P = 0.001). Stratified analysis showed EVF group with a baseline Alberta Stroke Program Early CT (ASPECT) score of ≤ 8 exhibited a higher risk of unfavorable outcomes compared with patients in the non-EVF group (OR = 2.64, 95%CI [1.03-6.73], P = 0.042). Mediation analysis indicated that malignant brain edema accounted for 35.42% of the correlation between EVF and unfavorable outcomes. CONCLUSIONS: This study establishes EVF as an independent risk factor for unfavorable outcomes after MT in AIS. Therefore, EVF in conjunction with a low ASPECT score provides essential insights for identifying patients at high risk for unfavorable 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 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.262
Teacher spread0.253 · 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 teacher head, not a consensus.

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

Citations5
Published2025
Admission routes1
Has abstractyes

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