Laboratory-Based Surveillance of Nontyphoidal <i>Salmonella</i> Infections in China
Bibliographic record
Abstract
Foodborne infections are an important public health problem in China. In 2008, we conducted surveillance for laboratory-confirmed nontyphoidal Salmonella to monitor trends for this infection in China and to build capacity for rapid detection and response to foodborne outbreaks. Salmonella isolates from patients with diarrhea were sent from hospitals to local public health laboratories for confirmation, serotyping, and antimicrobial susceptibility testing. A total of 126 hospitals in 44 cities and counties from 8 provinces provided isolates and epidemiologic data for analysis. Of 23,140 stool specimens submitted to clinical laboratories, 662 (3%) grew Salmonella enterica. Salmonella were most commonly detected between April and October. The median age of infected patients was 27 years; 34% of infections occurred in patients <5 years old. Of the 662 isolates, we found 73 serotypes, of which serotype Enteritidis (31%) and serotype Typhimurium (26%) were the most common. The prevalence of resistance was high for clinically important antimicrobial agents, including ampicillin (41%) and ciprofloxacin (6%). More than 60% of isolates, including 35% of all Typhimurium, were resistant to three or more antimicrobial agents. In this first multiprovince surveillance report of laboratory-confirmed Salmonella infections in China, we found that Enteritidis and Typhimurium are the most common serotypes and that efforts to reduce antimicrobial resistance among Salmonella in China are needed. Although no outbreaks were detected using this system, efforts to improve this system's capacity to do so are underway.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".