Travellers’ diarrhoea – solidifying our knowledgebase
Bibliographic record
Abstract
PURPOSE OF REVIEW: Travellers' diarrhoea remains one of the most common diseases amongst international travellers. However, significant uncertainty remains about the most effective strategies for its prevention and management. This review summarises recent advances in travellers' diarrhoea epidemiology, diagnostics, and management, focusing on new severity definitions, the impact of molecular diagnostics, antimicrobial resistance, and postinfectious sequelae. RECENT FINDINGS: The incidence of travellers' diarrhoea remains substantial although much of this is attributable to mild disease. Viral travellers' diarrhoea is more frequently recognised due to the improved sensitivity of molecular diagnostics. Advances in microbiome research reveal both acute and persistent disruption to the microbiota following travellers' diarrhoea and antibiotic use. New severity definitions incorporating functional impairment offer improved clinical relevance but consensus over use remains lacking. Nonabsorptive antibiotics and probiotics show promise for treatment and prevention, but antimicrobial resistance continues to rise. Postinfectious irritable bowel syndrome (IBS) significantly impacts the recovery of some travellers' diarrhoea patients. SUMMARY: Consensus on severity definitions is needed to support successful research into new vaccines and therapeutics. Surveillance of resistance, research into microbiome disruption and recovery, and development of vaccines and probiotics are key priorities. Better pathophysiological understanding and new intervention strategies are required to help alleviate the suffering of post-travellers' diarrhoea IBS.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".