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Record W2087949311 · doi:10.1586/erc.09.167

Cabergoline therapy for prolactinomas: is valvular heart disease a real safety concern?

2009· article· en· W2087949311 on OpenAlexaff
Sophie Vallette, Karim Serri, Omar Serri

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2009
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsCentre Hospitalier de l’Université de MontréalHôpital du Sacré-Cœur de MontréalUniversité de MontréalHôpital Notre-Dame
Fundersnot available
KeywordsCabergolineMedicinevalvular heart diseaseInternal medicineAsymptomaticDopamine agonistDiscontinuationAgonistHormoneProlactin

Abstract

fetched live from OpenAlex

Dopamine agonists (DAs) are the first-line therapy for the treatment of hyperprolactinemia, with cabergoline, an ergot-derived selective D(2)-receptor agonist, being the preferred and most widely used drug. Recent studies reported cardiac valve regurgitations in patients with Parkinson's disease treated with high doses of DA, raising concerns about the safety of cabergoline in patients with hyperprolactinemia. To date, seven case-control studies have examined the potential association between cardiac valvular abnormalities and cabergoline therapy in patients with hyperprolactinemia. Overall, a total of 463 patients exposed to low doses of cabergoline (mean cumulative doses: 204-443 mg) for a mean duration of 45-79 months have been included in these studies. Patients in all the studies were asymptomatic without clinical signs of cardiac disease. Six studies did not show any association between cabergoline therapy and clinically relevant valvular regurgitation, whereas one study found an increased rate of moderate tricuspid regurgitation. In this report, we review and discuss the results of these studies and emphasize the limitations of the methodology used in the published literature. The clinical significance of the present findings has yet to be confirmed by future larger prospective studies with rigorous echocardiographic protocols and prolonged duration of follow-up.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
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 head0.026
GPT teacher head0.330
Teacher spread0.304 · 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

Citations17
Published2009
Admission routes1
Has abstractyes

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