Influenza-Like Illness in Travelers to the Developing World
Bibliographic record
Abstract
Travelers to developing regions are at risk for development of influenza-like illness (ILI). Little is known of traveler and trip characteristics associated with the development of ILI. TravMil is a prospective observational study, enrolling subjects presenting to six military travel clinics or predeployment-screening sites. We analyzed pre- and post-travel surveys from travelers visiting regions outside of the continental United States, Western or Northern Europe, Canada, Australia, or New Zealand between January 2010 and March 2016. Influenza-like illness was defined as a self-reported fever associated with either sore throat or cough. Trip and traveler characteristics were analyzed to determine risk factors for the development of ILI. Two thousand nine hundred and thirty-two trips were recorded (55% male, median age 45 years, 69% white, 51% on vacation, median travel duration 17 days). The 2,337 trips included the number of self-reported influenza vaccinations in the preceding 5 years (median 5). Eleven percent of the trips were complicated by an ILI lasting a median of 5 days; 70% and 17% of these reported upper and lower respiratory tract infection, respectively, and 12% reported both. On multivariate analysis, increased risk of ILI was associated with female gender (odds ratio [OR]: 1.60 [confidence interval (CI): 1.25–2.05], P < 0.01), age (years) (OR: 1.01 [CI: 1.01–1.02], P < 0.01); and duration of travel (days) (OR: 1.01 [CI: 1.00–1.01], P < 0.01). Influenza-like illness is common in travelers, regardless of traveler characteristics, purpose of travel, destination, or season of year. Female gender, older age, and longer duration of travel were associated with an increased risk of ILI. Additional tools and strategies are needed to prevent ILI in international 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".