Infographics and Visual Abstracts
Bibliographic record
Abstract
Medical educators teach and disseminate information to learners who are often in time-pressured clinical learning environments that can limit their ability to process and understand information. Heightening this challenge is the ongoing need for learners to access, identify, and apply relevant information from a high volume of new or existing literature. Creating and using digestible visual summaries of high-yield takeaways can overcome some of these challenges. However, most educators are unaware of strategies and tools for creating and disseminating concise, high-quality visual summaries.Infographics provide a visual representation of information, and visual abstracts are a subset of infographics used to synopsize key findings from an article. Through intentional use of design elements and visual-spatial reorganization of content, readers can process complex information more easily. Visual representations are time-efficient and designed to engage the reader's visual processing capacity and decrease cognitive workload. In fact, studies have found that visual abstracts result in equivalent or increased knowledge transfer and retention when added to text.1,2 The use of infographics and visual abstracts on social media platforms is associated with higher engagement and Altmetric scores than the dissemination of medical literature citations alone.3 In a randomized controlled trial the use of a visual abstract resulted in articles being read nearly 3 times as often.4
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 | 0.004 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.236 | 0.042 |
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".