Illness in Travelers Visiting Friends and Relatives: A Review of the GeoSentinel Surveillance Network
Bibliographic record
Abstract
Travelers returning to their country of origin to visit friends and relatives (VFRs) have increased risk of travel-related health problems. We examined GeoSentinel data to compare travel characteristics and illnesses acquired by 3 groups of travelers to low-income countries: VFRs who had originally been immigrants (immigrant VFRs), VFRs who had not originally been immigrants (traveler VFRs), and tourist travelers. Immigrant VFRs were predominantly male, had a higher mean age, and disproportionately required treatment as inpatients. Only 16% of immigrant VFRs sought pretravel medical advice. Proportionately more immigrant VFRs visited sub-Saharan Africa and traveled for >30 days, whereas tourist travelers more often traveled to Asia. Systemic febrile illnesses (including malaria), nondiarrheal intestinal parasitic infections, respiratory syndromes, tuberculosis, and sexually transmitted diseases were more commonly diagnosed among immigrant VFRs, whereas acute diarrhea was comparatively less frequent. Immigrant VFRs and traveler VFRs had different demographic characteristics and types of travel-related illnesses. A greater proportion of immigrant VFRs presented with serious, potentially preventable travel-related illnesses than did tourist travelers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".