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Record W2912679709

Abstract 15658: Arginine-vasopressin Levels Are Increased in Heart Failure With Preserved Ejection Fraction and Correlate With LV Hypertrophy

2016· article· en· W2912679709 on OpenAlexaboutno aff
Julio A. Chirinos, Amer Ahmed Syed, Haideliza Soto‐Calderon, Izzah Vassim, Swapna Varakantam, Maheswara R Koppula, Scott Akers, Alexei Y. Bagrov, Wen Bin Wei, Edward G. Lakatta, Olga Fedorova

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHeart failureVasopressinEjection fractionInternal medicineCardiologyHeart failure with preserved ejection fractionEndocrinologyVasopressin receptorMagnetic resonance imagingMuscle hypertrophyAntidiureticReceptorAntagonistRadiology
DOInot available

Abstract

fetched live from OpenAlex

Abstract: Arginine-Vasopressin (AVP) exerts a variety of effects. in addition to its antidiuretic effect via V2 receptors in the kidneys, AVP has vasoconstrictive effects via its action on V1a receptors in the peripheral vasculature. Whether patients with heart failure and preserved ejection fraction (HFpEF) demonstrate increased levels of AVP, and whether this relates to left ventricular remodeling is unknown. Methods: We studied 26 subjects with heart failure and reduced ejection fraction (HFrEF), 28 subjects with HFpEF, and 95 control subjects without heart failure. AVP was measured ELISA (Cayman Chemicals ; USA). In a subgroup of subjects (n=110) we measured LV mass with magnetic resonance imaging (SSFP cine sequences), with LV segmentation using CMR42 (Circle CV Imaging; Calgary, Canada). Results: AVP levels were significantly greater in HFpEF (1.01; 95%CI=0.84 to 1.19)Compared to either HFrEF (0.72; 95%CI=0.58 to 0.87) or controls without HF (0.69; 95%CI=0.62 to 0.75; ANOVA P<0.0001). After adjustme...

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.415

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 communication0.0000.001
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 head0.040
GPT teacher head0.262
Teacher spread0.222 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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