Cardiospace: a novel visionary journal in the cardiovascular space
Bibliographic record
Abstract
Cardiovascular disease is one of the leading causes of death. In recent years, with advances in molecular biology, model animal, bioengineering and data science, the pathogenesis of cardiovascular disease has gradually been unveiled, and on top of this, it is even more exciting to see the emergence of potential diagnostic and therapeutic approaches. For instance, the pig-to-human heart transplant is a milestone for the treatment of patients with end-stage heart failure; introduction of PCSK9 inhibitors is considered to be revolutionary for myocardial infarction therapy. With great joy and excitement, we have launched a new journal, Cardiospace, which is a bilingual publishing platform for high-quality original manuscripts covering original basic and clinical research on all aspects of cardiovascular medicine. Cardiospace is a powerful vehicle for researchers to discover and disseminate sound, novel scientific evidence to advance visionary research on personalized care. Cardiospace also features fast and high-quality peer reviews and editorial decisions. Almost all major journals that accept papers covering original research on cardiovascular diseases often have long cycles for peer review and publication, but our goal is to enable researchers to publish rapidly and have open access to their accepted manuscripts. To the best of our knowledge, Cardiospace is the first bilingual journal to publish articles in both English and Chinese. We hope that this bilingual publishing platform can allow broader dissemination of the findings and make these original and inspiring works accessible to readers. Cardiospace is an international peer-reviewed open-access journal covering the latest research findings and therapeutical approaches for the diagnosis, therapy and prevention of cardiovascular disease. Topics include, but are not limited to, heart failure, cardiomyopathy, vascular disease, systemic hypertension, arrhythmia, preventive cardiology, pharmacological and non-pharmacological treatments, and new diagnostic techniques. This journal accepts original manuscripts such as articles, reviews, case reports, editorials and short communications. There are no restrictions on the length of the paper and the total number of the figures and tables. As the Co-Editor-in-Chief, I am grateful to Associate Professor Eliano Pio Navarese and other editorial board members for their willingness to work together for the development of this journal. Together with other editors, we will spare no effort to process all manuscripts rapidly and fairly. Cardiospace aims to be a leading publishing platform worldwide. We are hopeful that this journal will contribute to advancing cardiovascular science and developing novel diagnostic and therapeutic approaches for cardiovascular disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.049 | 0.022 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".