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Record W2512705701 · doi:10.3995/jstroke.23.338

Diagnosis and management of internal carotid artery diseases in Japan

2001· article· en· W2512705701 on OpenAlexaboutno aff
Kazuo Minematsu, Chiaki Yokota, Yasuhiro Hasegawa, Toshifumi Mannami, Takenori Yamaguchi

Bibliographic record

VenueNosotchu · 2001
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal carotid arteryCarotid endarterectomyStenosisCardiologyInternal medicineEndarterectomyRadiology

Abstract

fetched live from OpenAlex

Recent changes in lifestyle have caused an increase in extracranial internal carotid artery (ICA) lesions in Japan. The prevalence of severe ICA atherosclerosis with>50% stenosis in the Suita City was 4.4% (7.9% in men and 1.3% in women), being almost equal to that reported in developed western countries. Several clini-cal studies also indicated that atherothrombotic brain infarction particularly due to extracranial ICA lesions has rapidly been increasing in frequency in Japan. The ICA lesions have strong relationships to cardiovascu-lar risk factors and coronary heart diseases.Carotid ultrasonography is a rapid, noninvasive and accurate way to examine ICA lesions. Diffusion MRI studies will provide new knowledge how ICA lesions produce infarcts particularly in borderzone areas.The efficacy of carotid endarterectomy (CEA) in the management of severe ICA stenosis was established in US, Canada, and Europe, but not in Japan. Carotid artery stenting remains a matter of investigations. Clini-cal evidence on the effectiveness and limitations of such surgical interventions and medicla management with antithrombotic agents and HMG-CoA blockers should be collected for Japanese patients with ICA lesions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.250
Teacher spread0.238 · 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.

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

Citations0
Published2001
Admission routes1
Has abstractyes

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