Establishing Streptomycin Epidemiological Cut-Off Values for <i>Salmonella</i> and <i>Escherichia coli</i>
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it