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Record W4408574743 · doi:10.1148/radiol.241509

Guided Antiplatelet Therapy for Stent-Treated Intracranial Aneurysms: A Cluster-Randomized Trial

2025· article· en· W4408574743 on OpenAlexaff
Yangyang Zhou, Wenqiang Li, Jian Liu, Yisen Zhang, Shiqing Mu, Ruhang Xie, Qichen Peng, Bin Luo, Yuanli Zhao, Yang Wang, Ziqing Zhang, Yi‐Xin Lin, Pinyuan Zhang, Jiren Zhang, Liang Li, Xiangdong Yin, Fushun Xiao, Yunpeng Lin, Xiaoning Liu, Yang Bian, Jingwei Li, David Hasan, Timo Krings, Hongqi Zhang, Xinjian Yang

Bibliographic record

VenueRadiology · 2025
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineRandomized controlled trialAneurysmStentSurgeryPlatelet aggregation inhibitorEndovascular treatmentRadiologyAspirinInternal medicine

Abstract

fetched live from OpenAlex

For participants with intracranial aneurysm who underwent stent treatment, light transmission aggregation–guided antiplatelet therapy helped reduce ischemic events without increasing bleeding risks.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.022
GPT teacher head0.307
Teacher spread0.285 · 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 designRandomized trial
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

Citations6
Published2025
Admission routes1
Has abstractyes

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