‘Why was my paper rejected?’: understanding editorial decisions in a high-impact cardiovascular nursing journal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the past decade, medical and health journals have seen a notable increase in the number of manuscript submissions. This surge reflects the growing body of global research and the drive among healthcare professionals and researchers to share and report their clinical and scientific findings. Despite the digital revolution, which has enabled broader dissemination of information, reputable scientific journals still operate with constraints on how many articles they can publish annually. Quality assurance, editorial standards, and reader engagement remain top priorities, limiting the volume of accepted work. As a result, the role of editors has grown increasingly complex: they have to sift through a rising tide of submissions to identify and publish only the most impactful, rigorous, and relevant science. This editorial offers some insight into why many submissions, even those of reasonable quality, may not be accepted for publication. At the European Journal of Cardiovascular Nursing, the acceptance rate for unsolicited articles has declined over time and stands at approximately 15% to date (Central Illustration). This figure aligns with what is observed in many other high-impact journals. Each submission undergoes an initial editorial review, and about half of the manuscripts are rejected without being sent for external peer review. This ‘desk rejection’ process serves two important purposes: first, it allows authors to rapidly submit their work to another journal; second, it conserves the valuable time and expertise of our reviewers.
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.
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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it