Development of the Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide
Bibliographic record
Abstract
People often use infographics (also called visual or graphical abstracts) as a substitute for reading the full text of an article. This is a concern because most infographics do not present sufficient information to interpret the research appropriately and guide wise health decisions. The Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide aims to improve the completeness with which research findings of comparative studies are communicated and avoid research findings being misinterpreted if readers do not refer to the full text. The primary audience for the RIVA-C checklist and guide is developers of infographics that summarise comparative studies of health and medical interventions. The need for the RIVA-C checklist and guide was identified by a survey of how people use infographics. Possible checklist items were informed by a systematic review of how infographics report research. We then conducted a two-round, modified Delphi survey of 92 infographic developers/designers, researchers, health professionals and other key stakeholders. The final checklist includes 10 items. Accompanying explanation and both text and graphical examples linked to the items were developed and pilot tested over a 6-month period. The RIVA-C checklist and guide was designed to facilitate the creation of clear, transparent and sufficiently detailed infographics which summarise comparative studies of health and medical interventions. Accurate infographics can ensure research findings are communicated appropriately and not misinterpreted. By capturing the perspectives of a wide range of end users (eg, authors, informatics editors, journal editors, consumers), we are hopeful of rapid endorsement and implementation of RIVA-C.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".