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Sympathetic Responses to Atrial Natriuretic Peptide in Patients with Congestive Heart Failure

2000· article· en· W2333554266 on OpenAlexaff
Eduardo Ribeiro de Azevêdo, Gary E. Newton, Andrea B. Parker, John S. Floras, John D. Parker

Bibliographic record

VenueJournal of Cardiovascular Pharmacology · 2000
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineHeart failureInternal medicineAtrial natriuretic peptideSodium nitroprussideCardiologyVasodilationBlood pressureHemodynamicsNorepinephrineDiastoleNatriuretic peptideCardiac outputAnesthesiaNitric oxide

Abstract

fetched live from OpenAlex

Previous studies have shown that atrial natriuretic peptide (ANP) has relative sympathoinhibitory effects that are of potential benefit in patients with congestive heart failure (CHF). In this study, cardiac and systemic sympathetic responses to ANP were evaluated and compared with responses to sodium nitroprusside (SNP) in patients with CHF. Right- and left-heart hemodynamics were obtained simultaneously with cardiac (CANESP) and total body (TBNESP) norepinephrine spillover; these were measured by using the radiotracer technique. Reductions in arterial blood pressure and cardiac filling pressures were similar with both drugs. ANP and SNP caused a significant and similar increase in TBNESP. Mean values for CANESP did not change in either group, but the response of individual patients was dependent on the effect on diastolic blood pressure (r = -0.71, p<0.01). These results do not provide evidence for a sympathoinhibitory effect of ANP, but suggest that in patients with CHF, cardiac sympathoexcitatory response to arterial baroreceptor unloading may be countered by a potential sympathoinhibitory effect caused by a reduction in cardiac filling pressures. In the setting of CHF, vasodilator therapy may decrease cardiac sympathetic activity if systemic hypotension is avoided.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.588

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 science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.259
Teacher spread0.252 · 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 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

Citations18
Published2000
Admission routes1
Has abstractyes

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