An epidemiological framework for improving the accuracy of whole-genome sequence-based antimicrobial resistance surveillance in <i>Salmonella</i>
Bibliographic record
Abstract
Whole-genome sequence-based surveillance of bacteria for determinants of antimicrobial resistance (AMR) promises many advantages over traditional, wet-lab approaches. However, adjustments to parameters used to identify genetic determinants from sequencing data can affect results and interpretation of the important determinants in circulation. Using a dataset of whole-genome sequences from 1633 isolates of Salmonella Heidelberg and S. Kentucky collected from surveillance of Canadian poultry production, we queried the genomic data using an in silico AMR detection tool, StarAMR, applying a range of parameter values required for the detection pipeline to test for differences in detection accuracy. We compared the results from each iteration to phenotypic antimicrobial susceptibility results, and generated estimates of sensitivity and specificity using regression models that controlled for the effects of multiple sampling events and variables, and interactions between covariates. Results from our analyses revealed small, yet significant effects of the input parameters on the sensitivity and specificity of the AMR detection tool, and these effects differed based on the serovar and drug class in question. Findings from this study may have implications for the incorporation of whole-genome sequence-based approaches to the surveillance of AMR determinants in bacteria sampled from food products and animals related to food production.
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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 | 0.031 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".