Characteristics and Spectrum of Disease Among Ill Returned Travelers from Pre- and Post-Earthquake Haiti: The GeoSentinel Experience
Bibliographic record
Abstract
To describe patient characteristics and disease spectrum among foreign visitors to Haiti before and after the 2010 earthquake, we used GeoSentinel Global Surveillance Network data and compared 1 year post-earthquake versus 3 years pre-earthquake. Post-earthquake travelers were younger, predominantly from the United States, more frequently international assistance workers, and more often medically counseled before their trip than pre-earthquake travelers. Work-related stress and upper respiratory tract infections were more frequent post-earthquake; acute diarrhea, dengue, and Plasmodium falciparum malaria were important contributors of morbidity both pre- and post-earthquake. These data highlight the importance of providing destination- and disaster-specific pre-travel counseling and post-travel evaluation and medical management to persons traveling to or returning from a disaster location, and evaluations should include attention to the psychological wellbeing of these travelers. For travel to Haiti, focus should be on mosquito-borne illnesses (dengue and P. falciparum malaria) and travelers' diarrhea.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".