Standardizing multidrug resistance definitions and visualizations to support surveillance across One Health
Bibliographic record
Abstract
OBJECTIVE: This study aimed to understand the current use of visualizations for multidrug resistance (MDR) data across the One Health spectrum and the visualization preferences and definitions of MDR used by antimicrobial resistance experts, with emphasis on the animal health sector of One Health, which lacks standardized MDR definitions. METHODS: A rapid scoping review was conducted to synthesize current approaches to visualize MDR. Six databases and grey literature were searched with antimicrobial, resistance, surveillance, and figure or dashboard terms. An active machine learning model was used for the initial screening of references. An online survey was distributed to self-identified antimicrobial resistance experts, including questions about respondents' country of employment, job position, definitions of MDR, and preferences for MDR metrics and visualizations. RESULTS: Bar charts, visual antibiograms, heat maps, and network graphs were the most common visualizations employed in peer-reviewed publications, websites, and reports. Survey respondents preferred simplistic visualizations, such as line graphs and heat maps. Respondents used a variety of MDR definitions, although resistance to three or more antimicrobial categories was the most common. Some respondents advocated for the exclusion of intrinsic resistance in the definition, while others argued for its inclusion. CONCLUSIONS: Despite historic proposals for standardizing international definitions of MDR, a lack of consensus remains. Respondents also expressed different preferences for MDR visualizations. Some visualizations currently in use, such as network graphs, are complex and may be challenging to interpret. Harmonization of MDR definitions and optimization of visualizations are essential to facilitate comparisons across populations and studies.
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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 | 0.100 | 0.297 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".