Dermatologic conditions of the ill returned traveler: an analysis from the GeoSentinel Surveillance Network
Bibliographic record
Abstract
BACKGROUND: Skin disorders are common in travelers. Knowledge of the relative frequency of post-travel-related skin disorders, including their geographic and demographic risk factors, will allow for effective pre-travel counseling, as well as improved post-travel diagnosis and therapeutic intervention. METHODS: We performed a retrospective study using anonymous patient demographic, clinical, and travel-related data from the GeoSentinel Surveillance Network clinics from January 1997 through February 2006. The characteristics of these travelers and their itineraries were analyzed using SAS 9.0 statistical software. RESULTS: A skin-related diagnosis was reported for 4594 patients (18% of all patients seen in a GeoSentinel clinic after travel). The most common skin-related diagnoses were cutaneous larva migrans (CLM), insect bites including superinfected bites, skin abscess, and allergic reaction (38% of all diagnoses). Arthropod-related skin diseases accounted for 31% of all skin diagnoses. Ill travelers who visited countries in the Caribbean experienced the highest proportionate morbidity due to dermatologic conditions. Pediatric travelers had significantly more dog bites and CLM and fewer insect bites compared with their adult counterparts; geriatric travelers had proportionately more spotted fever and cellulitis. CONCLUSIONS: Clinicians seeing patients post-travel should be alert to classic travel-related skin diseases such as CLM as well as more mundane entities such as pyodermas and allergic reactions. To prevent and manage skin-related morbidity during travel, international travelers should avoid direct contact with sand, soil, and animals and carry a travel kit including insect repellent, topical antifungals, and corticosteroids and, in the case of extended and/or remote travel, an oral antibiotic with ample coverage for pyogenic organisms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".