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Record W4321748233 · doi:10.1002/ejhf.2806

February 2023 at a Glance: Focus on Pathophysiology and Treatment

2023· article· en· W4321748233 on OpenAlexaff
Daniela Tomasoni, Marianna Adamo, Marco Metra

Bibliographic record

VenueEuropean Journal of Heart Failure · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineRadiological weaponCardiac catheterizationGeneral surgeryInternal medicineSurgery

Abstract

fetched live from OpenAlex

Sympathetic activityAutonomic nervous system is a main target on novel interventions. [1]1][2][3][4] Badrov et al. 5 investigated determinants of augmented muscle sympathetic nerve activity (MSNA) in 177 heart failure (HF) patients and 658 healthy volunteers.MSNA was higher among HF patients, especially in men, with ischaemic cardiomyopathy and among those with sleep apnoea.Burst frequency was inversely associated with stroke volume, cardiac output, and peak oxygen consumption, and directly associated with norepinephrine and peripheral vascular resistance. Medical therapyEarly start of the four pillars of guideline-directed medical therapies (GDMT) in patients with HF and reduced EF (HFrEF) is strongly suggested.19,[21][22][23][24] A survey involving 615 cardiologists worldwide showed that the historical sequential approach (angiotensin-converting enzyme inhibitors or angiotensin receptor-neprilysin inhibitors first, beta-blockers second, mineralocorticoid receptor antagonists third, and sodium-glucose

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.309
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.3090.205

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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2023
Admission routes1
Has abstractyes

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