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Record W3007638568 · doi:10.1016/j.hest.2020.02.003

Intracerebral hemorrhage in translational research

2020· article· en· W3007638568 on OpenAlexaff
Ruiyi Zhang, Qian Bai, Yang Liu, Yan Zhang, Zhao‐Fu Sheng, Mengzhou Xue, V. Wee Yong

Bibliographic record

VenueBrain Hemorrhages · 2020
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNational Key Research and Development Program of China Stem Cell and Translational ResearchNational Natural Science Foundation of China
KeywordsIntracerebral hemorrhageMedicineTranslational researchStroke (engine)Intensive care medicineDiseaseSpontaneous intracerebral hemorrhagePathogenesisPathologyInternal medicineSubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

Intracerebral hemorrhage (ICH) is a serious stroke subtype with high morbidity and mortality. The prognosis of ICH is poor. In recent years, there have been many studies on how to improve the prognosis of ICH. This article mainly summarizes the research progress of ICH in translational research, including its risk factors and pathogenesis, course of disease, primary and secondary ICH brain injury, its prevention and treatment strategies.

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)0.059
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.059
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.071
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.008
Science and technology studies0.0020.005
Scholarly communication0.0090.009
Open science0.0030.007
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0170.006

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 head0.113
GPT teacher head0.382
Teacher spread0.269 · 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 designNot applicable
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

Citations13
Published2020
Admission routes1
Has abstractyes

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