Fever in Returned Travelers: Results from the GeoSentinel Surveillance Network
Bibliographic record
Abstract
BACKGROUND: Fever is a marker of potentially serious illness in returned travelers. Information about causes of fever, organized by geographic area and traveler characteristics, can facilitate timely, appropriate treatment and preventive measures. METHODS: Using a large, multicenter database, we assessed how frequently fever is cited as a chief reason for seeking medical care among ill returned travelers. We defined the causes of fever by place of exposure and traveler characteristics. RESULTS: Of 24,920 returned travelers seen at a GeoSentinel clinic from March 1997 through March 2006, 6957 (28%) cited fever as a chief reason for seeking care. Of patients with fever, 26% were hospitalized (compared with 3% who did not have fever); 35% had a febrile systemic illness, 15% had a febrile diarrheal disease, and 14% had fever and a respiratory illness. Malaria was the most common specific etiologic diagnosis, found in 21% of ill returned travelers with fever. Causes of fever varied by region visited and by time of presentation after travel. Ill travelers who returned from sub-Saharan Africa, south-central Asia, and Latin America whose reason for travel was visiting friends and relatives were more likely to experience fever than any other group. More than 17% of travelers with fever had a vaccine-preventable infection or falciparum malaria, which is preventable with chemoprophylaxis. Malaria accounted for 33% of the 12 deaths among febrile travelers. CONCLUSIONS: Fever is common in ill returned travelers and often results in hospitalization. The time of presentation after travel provides important clues toward establishing a diagnosis. Preventing and promptly treating malaria, providing appropriate vaccines, and identifying ways to reach travelers whose purpose for travel is visiting friends and relatives in advance of travel can reduce the burden of travel-related illness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".