International mass gatherings and travel-associated illness: A GeoSentinel cross-sectional, observational study
Bibliographic record
Abstract
BACKGROUND: Travelers to international mass gatherings may be exposed to conditions which increase their risk of acquiring infectious diseases. Most existing data come from single clinical sites seeing returning travelers, or relate to single events. METHODS: Investigators evaluated ill travelers returning from a mass gathering, and presenting to a GeoSentinel site between August 2015 and April 2019, and collected data on the nature of the event and the relation between final diagnoses and the mass gathering. RESULTS: Of 296 ill travelers, 51% were female and the median age was 54 years (range: 1-88). Over 82% returned from a religious mass gathering, most frequently Umrah or Hajj. Only 3% returned from the Olympics in Brazil or South Korea. Other mass gatherings included other sporting events, cultural or entertainment events, and conferences. Respiratory diseases accounted for almost 80% of all diagnoses, with vaccine preventable illnesses such as influenza and pneumonia accounting for 26% and 20% of all diagnoses respectively. This was followed by gastrointestinal illnesses, accounting for 4.5%. Sixty-three percent of travelers reported having a pre-travel encounter with a healthcare provider. CONCLUSIONS: Despite this surveillance being limited to patients presenting to GeoSentinel sites, our findings highlight the importance of respiratory diseases at mass gatherings, the need for pre-travel consultations before mass gatherings, and consideration of vaccination against influenza and pneumococcal disease.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".