Bibliographic record
Abstract
OBJECTIVE: To review recent developments in the field of travel medicine and to outline the knowledge and resources family physicians need for providing health advice to travelers headed for tropical or developing countries. QUALITY OF EVIDENCE: Personal files; references from review articles and from a recent textbook of travel medicine; current guidelines on pretravel advice; and a review of the 1996 to 1999 MEDLINE database using "travel medicine" as a term and subject heading, "trave(l)lers' diarrhea" as a text word and subject heading, "immunization + travel," and "malaria + chemo prevention" were used as information sources. Priority was given to randomized controlled trials and recommendations of expert or national bodies. MAIN MESSAGE: Some elements of travel medicine, such as malaria chemoprophylaxis, have become more complex. Some valuable new preventive measures, such as hepatitis A vaccine, treated bed nets, and antimalarial drugs, have become available. Some health risks, such as cholera, have been overemphasized in the past, whereas others, such as tuberculosis and sexually transmitted diseases, have been underemphasized. Information sources relevant for providing travel health advice have improved and expanded. Canadian evidence-based guidelines addressing most important travel health issues are now available. CONCLUSIONS: Travel medicine is a rapidly evolving field. Physicians intending to provide health advice to travelers to high-risk parts of the world should be well prepared and have access to good, up-to-date information.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".