Epidemiology and etiology of Traveler's diarrhea in Bangkok, Thailand, a case-control study
Bibliographic record
Abstract
Traveler's diarrhea (TD) is a common health problem among visitors from developed to developing countries. Although global and regional estimates of pathogen distribution are available, the etiology of diarrhea among non-military travelers to Thailand is largely unknown.A prospective TD case-control study was conducted among adult travelers from developed countries at a prominent hospital in Bangkok, Thailand during 2001-2003. Stool samples were collected from acute TD cases and non-diarrheal controls and analyzed for bacterial, viral, and protozoan pathogens by microbiology, ELISA or PCR methods. Calculation of adjusted odd ratios for risk factors was performed by logistic regression using STATA statistical software.Stool samples were collected and analyzed from 389 TD cases and 400 non-diarrhea controls. At least one pathogen was detected in 227 (58%) cases and 124 (31%) controls. Plesiomonas (14%), Vibrio (14%), Campylobacter (14%), and norovirus (12%) were the most frequently isolated pathogens among cases and significantly associated with diarrhea at p = 0.006, p < 0.001, p < 0.001, p < 0.001, respectively. Shigella (3%) and ETEC (8%), detected in lower prevalence, also showed significant association with TD at p < 0.001 and p = 0.002, respectively. Travelers from East Asian countries had an increased risk of Vibrio infection (Crude odds ratio: 3.1, p-value = 0.001); travelers from the United States, Canada, and Europe had an increased risk of Campylobacter infection (Crude odds ratio: 2.6, p-value = 0.001); and travelers from Australia and New Zealand had an increased risk of Salmonella infection (Crude odds ratio: 3.2, p-value = 0.009).Etiology of TD in Thailand is mainly of bacterial origin. Plesiomonas, Vibrio, and norovirus are underappreciated diarrheagenic pathogens. In our study, the origin of the traveler plays an important role in diarrhea etiology. Understanding variations in TD severity and etiology among travelers from different regions warrants further study.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".