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Record W2160305626 · doi:10.3317/jraas.2006.019

Use of Valsartan in Post-Myocardial Infarction and Heart Failure Patients

2006· article· en· W2160305626 on OpenAlexaff
Peter P. Liu, Aldo P. Maggioni, Eric J. Velazquez

Bibliographic record

VenueJournal of the Renin-Angiotensin-Aldosterone System · 2006
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineValsartanCardiologyMyocardial infarctionInternal medicineHeart failureACE inhibitorBlockadeRenin–angiotensin systemAngiotensin-converting enzymeBlood pressureReceptor

Abstract

fetched live from OpenAlex

Left ventricular (LV) dysfunction and/or heart failure (HF) are frequent complications of hypertension and myocardial infarction (MI), placing affected patients at increased risk of significant morbidity and premature death. Given that the renin-angiotensin-aldosterone system (RAAS) is activated and of pathophysiological importance in such patients, a strong therapeutic rationale exists to target the main effector mechanism (that is, angiotensin II [Ang II]) in order to lessen the associated morbidity and mortality burden. Angiotensin-converting enzyme (ACE) inhibitors have been shown to reduce mortality and LV dysfunction and to slow disease progression in patients with HF, including high-risk, post-MI patients. However, ACE inhibitors (ACE-Is) may not provide optimal long-term RAAS blockade (a finding that is associated with a worse prognosis) and many patients are unable to tolerate such therapy (because of troublesome dry cough, for example). In contrast, Ang II receptor blockers (ARBs) may block the RAAS more completely than ACE-Is and appear to be better tolerated. Several large-scale trials gave evaluated the efficacy of ARBs in patients with LV dysfunction and/or HF (including high-risk, post-MI patients), and have confirmed their utility as an efficacious and well-tolerated alternative to ACE-Is in this setting.

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 score0.062
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.222
Teacher spread0.211 · 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

Citations4
Published2006
Admission routes1
Has abstractyes

Explore more

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