Establishing Streptomycin Epidemiological Cut-Off Values for <i>Salmonella</i> and <i>Escherichia coli</i>
Bibliographic record
Abstract
This study was conducted to elucidate the accuracy of the current streptomycin epidemiological cut-off value (ECOFF) for Escherichia coli and Salmonella spp. A total of 236 Salmonella enterica and 208 E. coli isolates exhibiting MICs between 4 and 32 mg/L were selected from 12 countries. Isolates were investigated by polymerase chain reaction for aadA, strA, and strB streptomycin resistance genes. Out of 236 Salmonella isolates, 32 (13.5%) yielded amplicons for aadA (n = 23), strA (n = 9), and strB (n = 11). None of the 60 Salmonella isolates exhibiting MIC 4 mg/L harbored resistance genes. Of the Salmonella isolates exhibiting MICs 8 mg/L, 16 mg/L, and 32 mg/L, 1.6%, 15%, and 39%, respectively, tested positive for one or more genes. For most monitoring programs, the streptomycin ECOFF for Salmonella is wild type (WT) ≤32 or ≤16 mg/L. A cut-off value of WT ≤32 mg/L would have misclassified 13.5% of the strains as belonging to the WT population, since this proportion of strains harbored resistance genes and exhibited MICs ≤32 mg/L. Out of 208 E. coli strains, 80 (38.5%) tested positive for aadA (n = 69), strA (n = 18), and strB (n = 31). Of the E. coli isolates exhibiting MICs of 4 mg/L, 8 mg/L, 16 mg/L, and 32 mg/L, 3.6%, 17.6%, 53%, and 82.3%, respectively, harbored any of the three genes. Based on the European Committee on Antimicrobial Susceptibility Testing guidelines (ECOFF ≤16 mg/L), 25% of the E. coli strains presenting MIC ≤16 mg/L would have been incorrectly categorized as belonging to the WT population. The authors recommend an ECOFF value of WT ≤16 mg/L for Salmonella and WT ≤8 mg/L for E. coli.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".