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Record W2139097765 · doi:10.5041/rmmj.10078

Novel Strategies for the Treatment of Heart Failure

2012· article· en· W2139097765 on OpenAlexfundno aff
Izhak Kehat

Bibliographic record

VenueRambam Maimonides Medical Journal · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsnot available
FundersCentre for Interdisciplinary Research in Rehabilitation
KeywordsHeart failureMedicineExcitation–contraction couplingBioinformaticsReceptorCardiologyNeuroscienceInternal medicineBiologyCalcium

Abstract

fetched live from OpenAlex

Heart failure is a leading cause of morbidity and mortality with a prevalence that is rising throughout the world. Currently the pharmaceutical therapy of heart failure is mainly based on inhibition of the neurohumoral pathways that are activated secondary to the deterioration of cardiac function, and diuretics to alleviate the salt and water overload. With our increasing understanding of the pathophysiology of heart failure, it is now clear that the macroscopic and functional changes in the failing heart result from remodeling at the cellular, interstitial, and molecular levels. Therefore, emerging therapies propose to intervene directly in the remodeling process at the cellular and the molecular levels. Here, several experimental strategies that aim to correct the abnormalities in receptor and post-receptor-function, calcium handling, excitation and contraction coupling, signaling, and changes in the extra-cellular matrix in the failing heart will be discussed. These novel approaches, aiming to reverse the remodeling process at multiple levels, may appear on the clinical arena in the coming years.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.019
GPT teacher head0.287
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2012
Admission routes1
Has abstractyes

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