Clinical Characteristics and Outcomes Among Travelers With Severe Dengue
Bibliographic record
Abstract
BACKGROUND: mosquitoes and is an important cause of illness worldwide. Data on the severity of travel-associated dengue illness are limited. OBJECTIVE: To describe the epidemiology, clinical characteristics, and outcomes among international travelers with severe dengue or dengue with warning signs as defined by the 2009 World Health Organization classification (that is, complicated dengue). DESIGN: Retrospective chart review and analysis of travelers with complicated dengue reported to GeoSentinel from January 2007 through July 2022. SETTING: 20 of 71 international GeoSentinel sites. PATIENTS: Returning travelers with complicated dengue. MEASUREMENTS: Routinely collected surveillance data plus chart review with abstraction of clinical information using predefined grading criteria to characterize the manifestations of complicated dengue. RESULTS: = 2) were reported. Of 44 patients with serologic data, 32 confirmed cases were classified as primary dengue (IgM+/IgG-) and 12 as secondary (IgM-/IgG+) dengue. LIMITATIONS: Data for some variables could not be retrieved by chart review for some patients. The generalizability of our observations may be limited. CONCLUSION: Complicated dengue is relatively rare in travelers. Clinicians should monitor patients with dengue closely for warning signs that may indicate progression to severe disease. Risk factors for developing complications of dengue in travelers need further prospective study. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention, International Society of Travel Medicine, Public Health Agency of Canada, and GeoSentinel Foundation.
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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) | 0.002 | 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".