The Journey to a Successful Illustrated Review
Bibliographic record
Abstract
Illustrated review articles, rooted in scientific rigor, are made up of "capsules" or panels of visuals that together provide an up-to-date overview of a topic. Illustrated reviews aim to provide a more accessible format than traditional written reviews to facilitate more effective knowledge translation and dissemination. However, the novelty of this format can dissuade prospective authors due to uncertainty and lack of comfort. To remedy this uncertainty, we have summarized the journey of developing an illustrated review, from identifying an appropriate topic to submitting the final manuscript for peer review. We highlight the importance of approaching an illustrated review from a storytelling perspective, and encouraging authors to keep their audience in mind when picking a theme or characters. We provide storyboard considerations and simplify graphic design principles to develop an outline and line draft for the illustrated review. We list programs available to authors to demystify creating attractive and engaging scientific visuals. Finally, we provide information on choosing colors or fonts and where to find copyright-free icons, graphics, illustrations, and pictures. This review provides prospective authors with the knowledge, tools, and resources to create an effective illustrated review article. If there is difficulty with the links embedded within the document please download the full PDF.
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 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 | 0.035 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.031 | 0.020 |
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".