Malaria in Travelers: A Review of the GeoSentinel Surveillance Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Malaria is a common and important infection in travelers. METHODS: We have examined data reported to the GeoSentinel surveillance network to highlight characteristics of malaria in travelers. RESULTS: A total of 1140 malaria cases were reported (60% of cases were due to Plasmodium falciparum, 24% were due to Plasmodium vivax). Male subjects constituted 69% of the study population. The median duration of travel was 34 days; however, 37% of subjects had a travel duration of < or =4 weeks. The majority of travellers did not have a pretravel encounter with a health care provider. Most cases occurred in travelers (39%) or immigrants/refugees (38%). The most common reasons for travel were to visit friends/relatives (35%) or for tourism (26%). Three-quarters of infections were acquired in sub-Saharan Africa. Severe and/or complicated malaria occurred in 33 cases, with 3 deaths. Compared with others in the GeoSentinel database, patients with malaria had traveled to sub-Saharan Africa more often, were more commonly visiting friends/relatives, had traveled for longer periods, presented sooner after return, were more likely to have a fever at presentation, and were less likely to have had a pretravel encounter. In contrast to immigrants and visitors of friends or relatives, a higher proportion (73%) of the missionary/volunteer group who developed malaria had a pretravel encounter with a health care provider. Travel to sub-Saharan Africa and Oceania was associated with the greatest relative risk of acquiring malaria. CONCLUSIONS: We have used a global database to identify patient and travel characteristics associated with malaria acquisition and characterized differences in patient type, destinations visited, travel duration, and malaria species acquired.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it