Illness in Travelers Returned From Brazil: The GeoSentinel Experience and Implications for the 2014 FIFA World Cup and the 2016 Summer Olympics
Bibliographic record
Abstract
BACKGROUND: Brazil will host the 2014 FIFA World Cup and the 2016 Olympic and Paralympic Games, events that are expected to attract hundreds of thousands of international travelers. Travelers to Brazil will encounter locally endemic infections as well as mass event-specific risks. METHODS: We describe 1586 ill returned travelers who had visited Brazil and were seen at a GeoSentinel Clinic from July 1997 through May 2013. RESULTS: The most common travel-related illnesses were dermatologic conditions (40%), diarrheal syndromes (25%), and febrile systemic illness (19%). The most common specific dermatologic diagnoses were cutaneous larva migrans, myiasis, and tungiasis. Dengue and malaria, predominantly Plasmodium vivax, were the most frequently identified specific causes of fever and the most common reasons for hospitalization after travel. Dengue fever diagnoses displayed marked seasonality, although cases were seen throughout the year. Among the 28 ill returned travelers with human immunodeficiency virus (HIV) infection, 11 had newly diagnosed asymptomatic infection and 9 had acute symptomatic HIV. CONCLUSIONS: Our analysis primarily identified infectious diseases among travelers to Brazil. Knowledge of illness in travelers returning from Brazil can assist clinicians to advise prospective travelers and guide pretravel preparation, including itinerary-tailored advice, vaccines, and chemoprophylaxis; it can also help to focus posttravel evaluation of ill returned travelers. Travelers planning to attend mass events will encounter other risks that are not captured in our surveillance network.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".