Determinants of Malaria Prophylaxis Among German Travelers to Kenya, Senegal, and Thailand
Bibliographic record
Abstract
To the Editor‐in‐Chief: We commend the article by Ropers and colleagues.1 We would like to draw attention to a related and important matter, that of the human immunodeficiency virus (HIV)–positive traveler. The advent of highly active antiretroviral therapy (HAART) has markedly improved the health and quality of life for these individuals, allowing them to live full and active lives, which includes travel to malaria‐endemic areas.2 The Ropers and colleagues study concludes that a large proportion of German travelers to tropical areas receive inadequate malaria prevention. This would be more worrying for HIV‐infected travelers as they are more prone to acquiring malaria and are at an increased risk of severe forms of malaria and death. Therefore, prevention of malaria is even more important in these individuals. Similar inadequacies with regard to lack of malaria prevention were shown in a 2006 survey in our department of 124 HIV‐infected patients. The survey showed that 46% of patients (N= 19 of 41) with recent travel had visited a country with a malaria risk.3 Interestingly, of 21 patients born in malaria‐endemic countries, 9 (43%) stated that their birth country was not at risk of malaria, a lack of awareness that may prevent seeking appropriate precautions prior to travel. These results suggest that a significant number of HIV patients travel to malaria‐endemic areas, but a portion of them may not be taking adequate chemoprophylaxis.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".