Clinical Pearls in travellers and migrants
Bibliographic record
Abstract
Between 43 and 79% of travellers fall ill.1 Although the risk and clinical manifestations of diseases encountered by travellers are best studied in large epidemiological studies through global networks combined with translational scientific research,2 individual case reports also play a pivotal role in advancing the art and science of travel medicine. Case reports expand the field of medical knowledge to disseminate best clinical practice and original research. Case reports may highlight new insights on pathogenesis or transmission routes to a disease. For example, the non-vector route of transmission of Zika virus was first described in a case report of a returning traveller: a US traveller who had acquired a Zika infection in Senegal in 2008 passed it on to his wife upon return home suggesting sexual transmission.3 This observation was made many years before the Zika outbreak attracted international attention in 2015. It was also the case report of a pregnant traveller from Serbia to Brazil in early 2016 that nailed the causal association between maternal Zika virus infection and congenital Zika syndrome.4
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".