Latent class comparison of test accuracy when evaluating antimicrobial susceptibility using disk diffusion and broth microdilution to test<i>Escherichia coli</i>and<i>Mannheimia haemolytica</i>isolates recovered from beef feedlot cattle
Bibliographic record
Abstract
The study objective was to use Bayesian latent class analysis to evaluate the accuracy of susceptibility test results obtained from disk diffusion and broth microdilution using bacteria recovered from beef feedlot cattle. Isolates of Escherichia coli and Mannheimia haemolytica were tested for susceptibility to ampicillin, ceftiofur, streptomycin, sulfisoxazole, tetracycline, and trimethoprim-sulfamethoxazole. Results showed that neither testing method was always or even generally superior to the other. Specificity (ability to correctly classify non-resistant isolates) was extremely high for both testing methods, but sensitivity (ability to correctly classify resistant isolates) was lower, variable in the drugs evaluated, and variable between the two bacterial species. Predictive values estimated using Bayesian Markov chain Monte Carlo models showed that the ability to predict true susceptibility status was equivalent for test results obtained with the two testing methods for some drugs, but for others there were marked differences between results obtained from disk diffusion and broth microdilution tests.
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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 | 0.043 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".