Bibliographic record
Abstract
Heart Rhythm Case Reports is now completing its eighth year in publication. As one of four official journals of the Heart Rhythm Society, this open-access journal continues to endorse the aims of publishing high-quality case reports that improve the care of patients with heart rhythm disorders by advancing research, education, and procedural skill and efficacy. The journal underwent several changes this year. The website has been redesigned to improve the presentation, visibility, and site navigation. The case series “A Case for Education” continued, with authors invited over social media channels to augment opportunities for authors who may not be reached by traditional methods. We also have started to invite authors to provide editorial commentary on accepted case reports that have unique educational, therapeutic, and management insights. And, we have invited Dr Mohita Singh, an electrophysiology fellow at the University of Texas Southwestern, to become our first Fellows-in-Training Section Editor, a 2-year position. Dr Singh will provide oversight and leadership of a new page specifically designed for fellows that will include articles on issues relevant to training, career pathways, navigating the evolving field of electrophysiology, and other education topics. The journal remains very healthy. Since inception we have made a final disposition on approximately 2800 manuscripts. The overall acceptance rate is 43% and has remained consistent year to year. With a few weeks to go in 2021 as I write this report, we have received over 400 manuscripts, making this the second-highest annual total to date. The top contributing countries include, in order: United States, Japan, China, United Kingdom, Canada, Spain, Belgium, Brazil, France, and Italy. This year we said farewell to Associate Editor Dr Daniel Morin, who has served in multiple capacities with the journal since the launch in 2014. His guidance and direction have been critical to journal promotion and website presentation, and for the review and editorial processing of hundreds of manuscripts in general electrophysiology. Dr Morin will begin a new opportunity with another electrophysiology journal and his talents will magnify their efforts for growth and improvement. I am grateful for the other Associate Editors similarly. I rely heavily on their reviews and advice on manuscripts in their respective areas of specialty within electrophysiology. These editors are Dr Jonathan Dukes, Dr Rakesh Gopinathannair, Dr Christina Miyake, and Dr Yasuo Okumura. All our work would not be possible without a fantastic editorial staff and the generous support of the Heart Rhythm Society. The journal continues to enjoy a rich international readership and broad social media impact. The Twitter account has grown from approximately 7366 followers last year to now 10,056 by December 2021. A corresponding Facebook page is used to reach a different audience and to allow dynamic conversations that may require longer commentary than allowed by Twitter; it grew significantly this year from just a few hundred followers to 2030 followers. We will continue to look at novel additional methods to share our articles and disseminate our shared learning and understanding of heart rhythm disorders broadly. As I conclude, I remain very grateful for the opportunity to serve all of you as Editor-in-Chief of Heart Rhythm Case Reports. It is a great pleasure for me to learn from your insight into heart rhythm disease management, and the interesting and innovative approaches to improve treatments and outcomes. I am humbled when I consider the hours of service rendered by those that are willing to review manuscripts. I recognize that you all have multiple demands for your time and that when you accept our invitations to review it represents a sacrifice you are making for the betterment of our authors, the education product, and the journal. We rely on these reviews to improve the case reports and their messages. As we often have many early-career or new authors submitting these case reports, your reviews are often one of their first experiences with the scientific process and peer review. I believe that the review process aids in the educational experience for all authors and helps us as we consider diverse viewpoints. As always, I remain hopeful that our product will continue to meet your expectations and needs and, if not, I welcome your suggestions (e-mail: [email protected] ) for improvement and look forward to another year of learning from each of you.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".