Exploring Methodological Issues in Modelling Antimicrobial Resistance using Generic Escherichia coli Isolates from Chicken Abattoir and Retail Meat Surveillance in Canada
Bibliographic record
Abstract
Comparisons were made of the performance of different regression models for analysis of annual variation in susceptibility of generic Escherichia coli isolates to ceftiofur, ampicillin and nalidixic acid from retail chicken surveillance. Secondly, impacts of using different multidrug resistance (MDR) classification metrics for the analysis of annual variation in MDR were evaluated using generic E.coli isolates from chicken abattoir surveillance. Antimicrobial susceptibility data were obtained from the Canadian Integrated Program for Antimicrobial Resistance Surveillance. Model assumptions were met using logistic and multinomial regression. Linear, tobit, ordinal and complementary log-log regression did not meet model assumptions and/or did not perform well. Significant annual variation in susceptibility to all three antimicrobials was identified with multinomial regression, whereas logistic regression only identified significant annual variation in susceptibility to ceftiofur. Both the prevalence of MDR and interpretation of the association between MDR, and year and region differed depending on the MDR classification metric used.
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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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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 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".