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Record W7097947770

Effect of Pimobendan or Benazepri l Hydrochloride on Survival Times in Dogs with Congest ive Heart Fai lure Caused by Natural ly Occurring Myxomatous Mitral Valve Disease: The QUEST Study

2015· article· en· W7097947770 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsHeart failureBenazeprilHazard ratioClinical endpointMitral valveHeart diseaseMitral regurgitationRandomized controlled trial
DOInot available

Abstract

fetched live from OpenAlex

Background: Myxomatous mitral valve disease (MMVD) continues to be an important cause of morbidity and mortality in geriatric dogs despite conventional therapy. Hypothesis: Pimobendan in addition to conventional therapy will extend time to sudden cardiac death, euthanasia for car-diac reasons, or treatment failure when compared with conventional therapy plus benazepril in dogs with congestive heart failure (CHF) attributable to MMVD. Animals: Two hundred and sixty client-owned dogs in CHF caused by MMVD were recruited from 28 centers in Europe, Canada, and Australia. Methods: A prospective single-blinded study with dogs randomized to PO receive pimobendan (0.4–0.6mg/kg/d) or ben-azepril hydrochloride (0.25–1.0mg/kg/d). The primary endpoint was a composite of cardiac death, euthanized for heart failure, or treatment failure. Results: Eight dogs were excluded from analysis. One hundred and twenty-four dogs were randomized to pimobendan and 128 to benazepril. One hundred and ninety dogs reached the primary endpoint; the median time was 188 days (267 days for pimobendan, 140 days for benazepril hazard ratio5 0.688, 95 % confidence limits [CL]5 0.516–0.916, P5.0099). The benefit of pimobendan persisted after adjusting for all baseline variables. A longer time to reach the endpoint was also associated with being a Cavalier King Charles Spaniel, requiring a lower furosemide dose, and having a higher creatinine concentration. In-

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 score0.036
Threshold uncertainty score0.592

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 head0.010
GPT teacher head0.282
Teacher spread0.272 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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