The <i>EJHF</i> Last Editor’s Legacy: How can a High Impact Factor be Built?
Bibliographic record
Abstract
The European Journal of Heart Failure (EJHF) has reached a high impact factor making it one of the most important cardiology journals. I discuss herein what could be the main causes of such high ranking. Publication of the European Society of Cardiology guidelines for the diagnosis and treatment of acute and chronic heart failure has had the most important role with a number of citations, which has been approximately 10 times that of the other most cited articles of the same year. Other position statements, reviews, design papers, and research articles about landmark topics have given major contributions. With respect to the different clinical presentations, articles about heart failure with preserved ejection fraction and about advanced heart failure have gained many citations. Epidemiology, biomarkers, medical treatment, and devices have attracted most of the interest. In conclusion, being able to look ahead and to publish what is going to become important remains a major challenge. That of EJHF has been a success story, to date, and learning from the past may help to build upon this achievement.
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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 | 0.026 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 0.014 |
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".