Prospective Analysis of Parasitic Infections in Canadian Travelers and Immigrants
Bibliographic record
Abstract
BACKGROUND: International travel is associated with increased risk of vector-borne illnesses, particularly malaria. The objective of this study was to prospectively assess the relative frequency of parasitic diseases in Canadian travelers and to characterize demographic and travel-related predictors of these infections. METHODS: Data on Canadians and new immigrants who crossed international borders and were seen in the Tropical Disease Unit of Toronto General Hospital between November 1997 and June 2003 were prospectively collected and entered into the GeoSentinel Surveillance Network database. RESULTS: Of 3,528 returned Canadian travelers and new immigrants in the database, 1,010 had a parasitic infection diagnosed. Mean age of the 3,528 travelers was 37.3 years, and 42.6% were male. Those diagnosed with parasitic infections were more likely than the remaining cohort to have been traveling for the purpose of immigration (21.1% vs 7.1%, p < 0.001), or visiting friends and relatives (VFR) (17.9% vs 11.8%, p < 0.01). Common parasitic infections included nonhistolytica amebiasis (N= 209), malaria (N= 143), cutaneous larva migrans (N= 105), giardiasis (N= 74), and schistosomiasis (N= 48). CONCLUSIONS: Parasitic infections occurred in 29% of Canadian travelers. New immigrants and VFRs are at increased risk for malaria, as well as protozoal and helminthic infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".