Bibliographic record
Abstract
In this issue of the Annals, ISHNE members Professors Bayes de Luna and Bacharova substantially contribute to our understanding of atrial pathology as revealed in the electrocardiographic P wave. In so doing, the extend the work that Professor Bayes de Luna began nearly 40 years ago. In this remarkable paper, they taxonomize the three patterns of typical advanced inter-atrial block (A-IAB), as well as describe atypical patterns (i.e. with P wave durations less than 120 ms but positive–negative polarity in the inferior leads), explain the differentiation between A-IAB and atrial enlargement, and make the case that A-IAB may be as important a predictor of future stroke as atrial fibrillation. For those unfamiliar with the phenomenon that has been dubbed “Bayes Syndrome” (the Professor modestly avoids this eponym), this paper is the perfect introduction. In other important developments at Annals, we have added two important individuals to our masthead. Professor Linda Johnson, MD, PhD Associate Professor, Cardiovascular Epidemiology, Lund University has joined us in the role of Associate Editor. Her work combines a profound understanding of cardiovascular epidemiology with an interest in ambulatory ECG monitoring for assessment of atrial fibrillation risk and autonomic dysfunction. I am very grateful that she has accepted our invitation and look to encourage submissions in her areas of expertise. In a first for the Annals, we welcome our new Social Media Editor, Andrés Felipe Miranda-Arboleda, MD. Andres hails from Medellín, Colombia, but is currently finishing his electrophysiology fellowship at Queen's University, Kingston, Ontario. Please be sure to look out for his posts on Twitter, WeChat, and other platforms to keep you informed about important new Annals publications as well as throw-backs to classic papers that have stood the test of time. Welcome aboard, Linda and Andres!
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".