Variations in the Prevalence of Strains Expressing an Extended‐Spectrum β‐Lactamase Phenotype and Characterization of Isolates from Europe, the Americas, and the Western Pacific Region
Bibliographic record
Abstract
To evaluate the prevalence of extended-spectrum beta-lactamase (ESBL)-producing strains among species of Enterobacteriaceae, a microdilution susceptibility test was performed with strains of Klebsiella pneumoniae, Escherichia coli, Proteus mirabilis, and Salmonella species that were isolated as part of the SENTRY project. The highest percentage of ESBL phenotype (defined as a minimum inhibitory concentration [MIC] > or =2 microg/mL for ceftazidime, ceftriaxone, or aztreonam) was detected among K. pneumoniae strains from Latin America (45%), followed by those from the Western Pacific region (25%), Europe (23%), the United States (8%), and Canada (5%). P. mirabilis and E. coli strains for which MICs of extended-spectrum cephalosporins or monobactams were elevated also were more prominent in Latin America. Testing with ceftazidime revealed more isolates with elevated MICs than did testing with ceftriaxone or aztreonam. ESBL strains showed high levels of co-resistance to aminoglycosides, tetracycline, trimethoprim-sulfamethoxazole, and ciprofloxacin. Imipenem remains highly effective against ESBL strains. Organisms expressing an ESBL are widely distributed worldwide, although prevalence rates are significantly higher in certain geographic regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".