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Record W3023528107 · doi:10.1152/ajpheart.00057.2020

Does vascular stiffness predict white matter hyperintensity burden in ischemic heart disease with preserved ejection fraction?

2020· article· en· W3023528107 on OpenAlexafffund
Christopher S. Balestrini, Baraa K. Al‐Khazraji, Neville Suskin, J. Kevin Shoemaker

Bibliographic record

VenueAmerican Journal of Physiology-Heart and Circulatory Physiology · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsSt Joseph's Health CareMcMaster UniversityWestern University
FundersCanadian Institutes of Health Research
KeywordsCardiologyMedicineEjection fractionInternal medicinePulse wave velocityArterial stiffnessHyperintensityStroke (engine)Stroke volumeWhite matterHeart failureRadiologyMagnetic resonance imagingBlood pressure

Abstract

fetched live from OpenAlex

This study found that patients with ischemic heart disease (IHD) with preserved ejection fraction and normal blood pressures exhibit greater carotid β-stiffness, as well as middle cerebral artery pulsatility and resistive indexes, than controls. White matter lesion volume (WMLv) was not different between vascular pathology groups. Cerebrovascular pulse wave transit time (ccPWTT) and carotid β-stiffness independently associate with WMLv in pooled participant data, suggesting that regardless of heart disease history, ccPWTT and β-stiffness are associated with structural white matter damage.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.240
Teacher spread0.231 · 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 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
Published2020
Admission routes2
Has abstractyes

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