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Record W4405080943 · doi:10.1097/hco.0000000000001191

Editorial introductions

2024· article· en· W4405080943 on OpenAlexaboutno aff

Bibliographic record

VenueCurrent Opinion in Cardiology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicContemporary Sociological Theory and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Current Opinion in Cardiology was launched in 1985. It is part of a successful series of review journals whose unique format is designed to provide a systematic and critical assessment of the literature as presented in the many primary journals. The field of cardiology is divided into 14 sections that are reviewed once a year. Each section is assigned a Section Editor, a leading authority in the area, who identifies the most important topics at that time. Here we are pleased to introduce the Journal's Editor-in-chief and Section Editors for this issue. EDITOR-IN-CHIEF Subodh VermaSubodh VermaDr Subodh Verma is a cardiac surgeon-scientist, Full Professor, and the Canada Research Chair in Cardiovascular Surgery. He is a Fellow of the Canadian Academy of Health Sciences, a past member of the Royal Society of Canada College of New Scholars, Artists and Scientists, and a past recipient of the Royal College of Physicians and Surgeons of Canada Gold Medal in Surgery. Dr Verma was named a Clarivate Highly Cited Researcher in Clinical Medicine in 2022 and 2023 and was bestowed the University of Calgary Cumming School Medicine Alumnus of Distinction Award for Research in 2023. He is the current Scientific Program Committee Chair for the Canadian Society of Cardiac Surgeons and continues to actively contribute to Canadian clinical practice guidelines. He had/has leadership roles in multiple contemporary global heart failure, cardiometabolic disease, and atherosclerosis trials. Dr Verma founded the CardioLink platform that united cardiac surgeons from across Canada to conduct robust clinical trials to better inform on surgical decision-making pathways. He also leads a dynamic pre-clinical and translational research team that leverages pre-clinical disease models and clinical trial-derived data to identify novel mediators of cardiometabolic diseases. SECTION EDITORS Wilber W. SuWilber W. SuDr Wilber Su is the Director of Cardiac Electrophysiology Banner- University Medical Center and Professor of Medicine at the University of Arizona, USA as well as Stanford University Medical Center, USA. He has a background in biomedical engineering degree from Massachusetts Institute of Technology, USA and received his training at Mayo Clinic Rochester, Minnesota, USA for medicine, cardiology, clinician investigator fellowship, and cardiac electrophysiology. He has been very high-volume operator and has served as training physician in both implantable cardiac devices and complex ablation, notably in atrial fibrillation. He is also active in research with currently multiple important clinical trials and is the national primary investigator for several research trials. He is recognized as the key opinion leader and thought leader around the world as the leader in complex ablation and is a key opinion leader in cryoballoon ablation. He also has taught, presented, and published on various cryoballoon techniques. He has authored “Best Practice Guidelines for Cryoballon Ablation”, and book chapters. In addition, he also serves as a primary teaching center for mapping and ablation of complex arrhythmias. He is also a member of the writing committee for American Heart Association and Heart Rhythm Society national guideline. Dr Su currently serves as the president and governor of the Arizona Chapter of American College of Cardiology. Martin Bødtker MortensenMartin Bødtker MortensenMartin Bødtker Mortensen MD PhD is Associate Professor at the Department of Cardiology at Aarhus University Hospital, Denmark, and Adjunct Associate Professor at the Ciccarone Center for The Prevention of Cardiovascular Disease, Johns Hopkins, Baltimore, USA. Dr Mortensen graduated from the medical school at Aarhus University in 2011 and have since then worked extensively with atherosclerotic cardiovascular disease both clinically and in his research. He obtained his PhD degree in 2015, based on experimental work performed in Dr Jacob Bentzons and Professor Erling Falks laboratory at the Department of Cardiology, Aarhus University Hospital. Dr Mortensen's research field covers both experimental research on the pathogenesis of atherosclerosis as well as epidemiological and clinical studies into the risk and prevention of atherosclerotic cardiovascular disease in humans. Particularly, he has worked extensively on the utility of using non-invasive imaging of subclinical atherosclerosis to identify individuals at low or high risk for development of future clinical disease. Dr Mortensen have published over 100 scientific articles, including in high-impact journals such as Lancet, BMJ, JACC, EHJ, Circulation, JAMA Cardiology and JCI. He is internationally recognized within the field and has received both national and international awards for his research. He has authored several book chapters regarding atherosclerosis, atherosclerotic cardiovascular disease, and dyslipidemias. Dr Mortensen treats patients with lipid disorders, including rare genetic dyslipidemias and familial hypercholesterolemia, in the advanced lipid-clinic at Aarhus University Hospital. Dr Mortensen serves as the chairman for the Preventive Cardiology group in Denmark (under the Danish Society of Cardiology). He has for many years participated in writing the Danish guidelines for treatment of dyslipidemia and prevention of cardiovascular disease issued by the Danish Society of Cardiology and is a regular speaker for the Danish Heart Foundation on the role of cholesterol in the development of atherosclerosis.

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)0.007
metaresearch head score (Gemma)0.064
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.187
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.064
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0060.002
Science and technology studies0.0030.002
Scholarly communication0.0080.005
Open science0.0030.003
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.1870.121

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 head0.121
GPT teacher head0.447
Teacher spread0.325 · 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
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
Published2024
Admission routes1
Has abstractyes

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