Bibliographic record
Abstract
Editorial fellowships provide an important opportunity for trainees and early career researchers to learn about the peer review process, manuscript handling, and journal operations 1 , 2 . This not only supports their own publishing practices but also prepares them to become robust reviewers, editorial board members, and editors 1 , 2 . Although several editorial fellowships exist, few have focused on transformative areas in medicine, such as artificial intelligence and digital health 3 , 4 . To address this gap, npj Digital Medicine established an Editorial Fellowship in 2021, teaching fellows how to assess and write about cutting-edge research in digital medicine 5 . The fellowship is managed by Editor-in-Chief Dr. Joseph Kvedar and Publishing Editor Dr. Tony Chen 5 . An open call for applications takes place each year and one candidate is selected to enter the program 5 . In the 2025–26 application cycle, there were 45 applications for one position. Applicants came from diverse backgrounds and geographic regions, with varied levels of training, areas of focus, and expertise. These descriptors highlight the strong and broad interest in the fellowship. This article discusses the structure and outcomes of the npj Digital Medicine Editorial Fellowship, along with my personal learning experiences as a recent graduate of the program.
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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 | 0.004 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".