Bacterial travellers’ diarrhoea: A narrative review of literature published over the past 10 years
Bibliographic record
Abstract
Travellers' diarrhoea (TD) is the most frequent illness experienced by international travellers to lower-income countries with bacterial agents considered to account for 80-90% of cases. In this review, we summarise evidence published on bacterial TD over the past 10 years, focusing on the epidemiology and aetiology of TD. Diarrhoeagenic Escherichia coli (DEC) continue to be the most commonly implicated bacteria in TD, although Enteropathogenic E. coli (EPEC) and Enteroaggregative E. coli (EAEC) now appear to be predominant where Enterotoxigenic E. coli (ETEC) was previously considered most prevalent globally. Where fluroquinolone resistance had primarily been documented for Campylobacter in Southeast Asia, widespread resistance has been observed in most regions of the world for multiple enteropathogens, including Shigella, Salmonella, ETEC and EAEC. Implementation of novel molecular methods for pathogen detection has led to identification of bacterial pathogens, including Clostridium difficile (with and without the use of prior antibiotics), Arcobacter species and Bacteroides fragilis, as aetiological agents in TD. The widespread resistance to first-line antibiotics in multiple bacterial enteropathogens warrants continued surveillance and re-evaluation of current treatment practices. Further investigations are required to determine the prevalence and geographical distribution of bacterial enteropathogens that have been more recently implicated in TD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".