Bibliographic record
Abstract
PURPOSE OF REVIEW: Travelers' diarrhea, affecting millions of travelers every year globally, continues to be a leading cause of morbidity despite advances in vaccination, prevention, and treatment. Complications of travelers' diarrhea often present to gastroenterologists and some patients followed by gastroenterologists are at higher risk of developing travelers' diarrhea. This review will provide an update on recent progress made in the epidemiology, pathogenesis, diagnosis, prevention, and treatment of travelers' diarrhea. RECENT FINDINGS: Most causes of travelers' diarrhea remain bacterial, but newly recognized pathogens are emerging. Patient-related and travel-related factors affect disease development risk and should guide prophylaxis and treatment. Although specific vaccines are being developed, they have not yet had a major impact on travelers' diarrhea, and understanding their roles and limitations is especially important. Prophylaxis and treatment of populations at risk (children, chronically ill patients, and those on immunosuppressive medications) remain challenging and require a tailored approach. SUMMARY: Travelers' diarrhea will continue to challenge patients and physicians despite the use of sanitation advice, prophylactic vaccines, and treatment with antibiotics. Effects may extend beyond the time of travel, such as postinfectious complications and exacerbation of preexisting disease. Future research should focus on novel strategies for reducing exposure to pathogens, vaccine development, early detection, and targeted treatments.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".