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Record W1824620580 · doi:10.2147/vhrm.s3467

Update on the use of Trandolapril in the management of cardiovascular disorders

2008· review· en· W1824620580 on OpenAlexaff
Anique Ducharme

Bibliographic record

VenueVascular Health and Risk Management · 2008
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsTrandolaprilMedicineCardiologyInternal medicineHeart failureCoronary artery diseaseAngiotensin-converting enzymeDiabetes mellitusACE inhibitorMyocardial infarctionNephropathyBlood pressureEndocrinology

Abstract

fetched live from OpenAlex

Trandolapril is a well known angiotensin converting enzyme (ACE) inhibitor with many cardiovascular (CV) indications. The objectives of this article are to review the pharmacokinetics and pharmacodynamics properties of trandolapril and to focus on its clinical relevance in cardiovascular medicine. Various populations have been studied in large clinical trials including patients with congestive heart failure (CHF) after an acute myocardial infarction (AMI), diabetics, patients with hypertension (HTN), stable coronary artery disease (CAD) and prevention of proteinuria. Long-term treatment with trandolapril in patients with reduced left ventricular function soon after AMI significantly reduced the risk of overall mortality, mortality from CV causes, sudden death, and the development of severe CHF. Treatment with trandolapril after AMI complicated by left ventricular dysfunction appears to be of considerable importance in patients with diabetes mellitus by saving lives and substantially reducing the risk of progression to severe CHF as well. Moreover, trandolapril reduces progression to proteinuria in high-risk patients. Some of the advantages of trandolapril over other ACE inhibitors are the wide spectrum of patient populations studied, the well established dosage and its proven trough-to-peak effect ratios permitting a safe once-a-day administration.

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: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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)0.0000.000

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 head0.081
GPT teacher head0.302
Teacher spread0.221 · 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
GenreReview

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
Published2008
Admission routes1
Has abstractyes

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