Analysis of Bacterial Pathogens Causing Acute Diarrhea on the Basis of Sentinel Surveillance in Shanghai, China, 2006–2011
Bibliographic record
Abstract
Acute diarrhea is the most common infectious disease worldwide and its causes vary from one region to another. We aimed to analyze the spectrum and epidemiological characteristics of pathogens from 22,386 outpatients with acute diarrhea on the basis of surveillance data from Shanghai, China, during 2006-2011. The following 8 pathogens were isolated and identified using standard methods: Salmonella, Shigella, Vibrio cholerae, V. parahaemolyticus, enteropathogenic Escherichia coli, enterotoxigenic E. coli, enteroinvasive E. coli, and enterohemorrhagic E. coli. In total, 2,234 strains of pathogens were obtained and the overall isolation rate of these 8 pathogens gradually decreased from 17.1% in 2006 to 7.4% in 2011. V. parahaemolyticus was the most frequently identified pathogen, followed by Shigella and Salmonella. The isolation rate of V. parahaemolyticus notably varied by season, whereas Salmonella and Shigella infections showed little seasonal variation. Age-related variation was also observed. V. parahaemolyticus infection occurred more often in patients aged 20-40 years. S. enterica serovar Enteritidis and S. flexneri were the most common serotypes of Salmonella and Shigella, respectively. The descending trend observed in the isolation rate of pathogens from the current surveillance suggests an urgent requirement or improvement.
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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 | 0.001 |
| 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 | 0.000 | 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 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".