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Record W2083056616 · doi:10.1097/hco.0b013e328329e9e8

Trying to succeed when the right ventricle fails

2009· review· en· W2083056616 on OpenAlexaff
Michael McDonald, Heather J. Ross

Bibliographic record

VenueCurrent Opinion in Cardiology · 2009
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineVentricleHeart failureRight ventricular failureCardiologyInotropeInternal medicinePulmonary hypertensionVentricular functionDiastoleIntensive care medicineBlood pressure

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Compared with the left ventricle, studies of right ventricular failure as a distinct clinical entity have lagged behind. Evolving appreciation of the prognostic significance of right ventricular dysfunction in the heart failure population and advances in noninvasive imaging have provided the impetus for recent investigation into the assessment and management of right ventricular failure. RECENT FINDINGS: Pulmonary hypertension and attendant right ventricular dysfunction are prevalent in patients with systolic and diastolic heart failure and are associated with poor survival. Simple echocardiographic and MRI indices of right ventricular function relate to prognosis and may also be useful in following response to therapy. Management of acute and chronic right ventricular failure is largely empiric and is focused on treating the underlying cause along with judicious use of diuretics and inotropes. The use of left ventricular assist devices to help treat pulmonary hypertension in heart failure is an emerging strategy in transplant-eligible patients. SUMMARY: Right ventricular failure is clinically significant and merits further dedicated study. Parameters of right ventricular dysfunction can be assessed noninvasively. An approach to the management of acute and chronic right ventricular failure should take into consideration novel pharmacologic and device-based therapies.

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 score0.964
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.161
GPT teacher head0.445
Teacher spread0.284 · 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

Citations34
Published2009
Admission routes1
Has abstractyes

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