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Predictors of parenchymal hemorrhage after endovascular treatment in acute ischemic stroke: data from ANGEL-ACT Registry

2022· article· en· W4205970937 on OpenAlexaboutno aff
Dapeng Sun, Baixue Jia, Xu Tong, Peter Kan, Xiaochuan Huo, Anxin Wang, ­ Raynald, Gaoting Ma, Ning Ma, Feng Gao, Dapeng Mo, Ligang Song, Xuan Sun, Lian Liu, Yiming Deng, Xiaoqing Li, Bo Wang, Gang Luo, Yongjun Wang, Zeguang Ren, Zhongrong Miao

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsMedicineModified Rankin ScaleInternal medicineStroke (engine)Logistic regressionAngioplastyIncidence (geometry)Neutrophil to lymphocyte ratioStenosisComplicationSurgeryGastroenterologyCardiologyIschemic strokeIschemiaLymphocyte

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.037
GPT teacher head0.276
Teacher spread0.240 · 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

Citations17
Published2022
Admission routes1
Has abstractyes

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