Clostridium difficile infection in returning travellers
Bibliographic record
Abstract
Background: There is increasing recognition of the contribution of community-acquired cases to the global burden of Clostridium difficile infection (CDI). The epidemiology of CDI among international travellers is poorly understood, and factors associated with international travel, such as antibiotic use and changes in gut microbiota, could potentially put travellers at higher risk. Methods: We summarized demographic, travel-associated and geographic characteristics of travellers with CDI in the GeoSentinel database from 1997 to 2015. We also surveyed GeoSentinel sites to compare various testing indications, approaches, and diagnostic modalities. Results: We identified 260 GeoSentinel records, including 187 that satisfied criteria for analysis (confirmed cases in non-immigrant travellers aged >2 years, seen <12 weeks post-travel). CDI was reported in all age groups and in travellers to all world regions; the largest proportions of cases having destinations in Asia (31%), Central/South America or the Caribbean (30%) and Africa (24%). Our site survey revealed substantial heterogeneity of testing approaches between sites; the most commonly used test was the C. difficile toxin gene PCR. Conclusions: CDI is encountered in returning international travellers, although there is considerable variability in testing practices. These data underscore the importance of awareness of C. difficile as a potential cause of travel-associated diarrhoea.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".