A tale of two labs: Comparing antimicrobial resistance data in pets across commercial and academic diagnostic laboratories
Bibliographic record
Abstract
Antimicrobial resistance *AMR) presents significant challenges in veterinary medicine, necessitating accurate surveillance to inform effective mitigation strategies. Most resistance estimates for cats and dogs are based on a single data source, typically university-affiliated diagnostic laboratories *UADLs), which may limit their generalizability. This study is the first to quantitatively compare AMR data from a UADL and a commercial diagnostic laboratory *CDL) by analyzing antimicrobial susceptibility testing *AST) results for Escherichia coli and Staphylococcus pseudintermedius in cats and dogs from New York State between 2019 and 2022. The analysis focused on first-line and higher-tier antimicrobials and revealed a tendency for the UADL data to observe lower susceptibility rates than the CDL. However, the extent of this difference varied by bacteria-antimicrobial combination, geographic region, and time. A secondary objective was to develop and test a novel Shiny application designed to harmonize and prepare data for comparison without exchanging raw data, addressing several data-sharing concerns that could limit collaboration. These findings highlight how variations in data sources can affect resistance estimates and interpretations. By identifying similarities and differences, this study underscores the importance of considering data source characteristics when analyzing and applying AMR surveillance reports. Integrating data from multiple sources may provide a more balanced and representative understanding of resistance patterns, thereby supporting more effective surveillance and decision-making in companion animal medicine. Here, we demonstrate that user-friendly analysis tools can support data integration without requiring raw data to be publicly available or shared between institutions.
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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 | 0.034 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".