Illness in Long-Term Travelers Visiting GeoSentinel Clinics
Bibliographic record
Abstract
Length of travel appears to be associated with health risks. GeoSentinel Surveillance Network data for 4,039 long-term travelers (trip duration >6 months) seen after travel during June 1, 1996, through December 31, 2008, were compared with data for 24,807 short-term travelers (trip duration <1 month). Long-term travelers traveled more often than short-term travelers for volunteer activities (39.7% vs. 7.0%) and business (25.2% vs. 13.8%). More long-term travelers were men (57.2% vs. 50.1%) and expatriates (54.0% vs. 8.9%); most had pretravel medical advice (70.3% vs. 48.9%). Per 1,000 travelers, long-term travelers more often experienced chronic diarrhea, giardiasis, Plasmodium falciparum and P. vivax malaria, irritable bowel syndrome (postinfectious), fatigue >1 month, eosinophilia, cutaneous leishmaniasis, schistosomiasis, and Entamoeba histolytica diarrhea. Areas of concern for long-term travelers were vector-borne diseases, contact-transmitted diseases, and psychological problems. Our results can help prioritize screening for and diagnosis of illness in long-term travelers and provide evidence-based pretravel advice.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".