Evaluating the Accuracy and Design of Visual Abstracts in Academic Surgical Journals
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
OBJECTIVE: The objective of this study was to assess the quality and accuracy of visual abstracts published in academic surgical journals. BACKGROUND: Visual abstracts are commonly used to disseminate medical research findings. They distill the key messages of a research article, presenting them graphically in an engaging manner so that potential readers can decide whether to read the complete manuscript. METHODS: We developed the Visual Abstract Assessment Tool based upon published guidelines. Seven reviewers underwent iterative training to apply the tool. We collected visual abstracts published by 25 surgical journals from January 2017 to April 2021; those corresponding to systematic reviews without meta-analysis, conference abstracts, narrative reviews, video abstracts, or nonclinical research were excluded. Included visual abstracts were scored on accuracy (as compared with written abstracts) and design, and were given a "first impression" score. RESULTS: Across 25 surgical journals 1325 visual abstracts were scored. We found accuracy deficits in the reporting of study design (35.8%), appropriate icon use (49%), and sample size reporting (69.2%), and design deficits in element alignment (54.8%) and symmetry (36.1%). Overall scores ranged from 9 to 14 (out of 15), accuracy scores from 4 to 8 (out of 8), and design scores from 3 to 7 (out of 7). No predictors of visual abstract score were identified. CONCLUSION: Visual abstracts vary widely in quality. As visual abstracts become integrated with the traditional components of scientific publication, they must be held to similarly high standards. We propose a checklist to be used by authors and journals to standardize the quality of visual abstracts.
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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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