Preparing patients to travel abroad safely. Part 1: Taking a travel history and identifying special risks.
Bibliographic record
Abstract
OBJECTIVE: To present for family physicians without access to a travel clinic and the Internet the questions to ask about the medical history and itinerary of their patients traveling abroad. To suggest ways to identify and advise high-risk patients. QUALITY OF EVIDENCE: MEDLINE searches from 1990 to November 1998 located 51 articles on travel and diabetes, 37 on travel and chronic obstructive pulmonary disease (COPD), 63 on travel and heart disease, 192 on travel and pregnancy, and 298 on travel with infants or children. Additional searches were undertaken in September 1999. The quality of evidence in most articles is level III (expert opinion). There are no randomized controlled trials of the best advice for family physicians to give travelers. MAIN MESSAGE: A history should include countries to be visited, planned activities, previous tropical travel, medical history, vaccination status, whether children are traveling, pregnancy status, and patients' opinions of the risks and precautions needed. Detailed advice should be given to reduce risks. The main causes of mortality abroad are existing cardiovascular conditions and accidents. High-risk conditions to be identified in travelers are cardiovascular illness, COPD, diabetes, immunodeficiency, pregnancy, and traveling with children. CONCLUSIONS: Patients with cardiovascular illness or COPD should be advised to avoid too much exertion while traveling. Detailed instruction should be given to diabetic patients on how to maintain stable glucose levels, to pregnant women on avoiding malarial infection, and to parents on protecting their children from infections and accidents.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".