Preparing patients to travel abroad safely. Part 4: Reducing risk of accidents, diarrhea, and sexually transmitted diseases.
Bibliographic record
Abstract
OBJECTIVE: To present evidence-based recommendations on traveling abroad safely so family physicians can advise travelers on how to reduce risk of accidents, diarrhea, and sexually transmitted diseases (STDs) and how to treat diarrhea themselves if medical care is unavailable. QUALITY OF EVIDENCE: A MEDLINE search from 1990 to November 1998 found 163 articles on travel and accidents, 504 on travel and diarrhea, and 42 on travel and STDs. Titles and abstracts were reviewed, and randomized controlled trials (RCTs) and systematic reviews were sought. The Cochrane Collaboration database of systematic reviews and meta-analyses was searched for studies relevant to family physicians. MAIN MESSAGE: For preventing diarrhea, RCTs demonstrate that bismuth subsalicylate, doxycycline, ciprofloxacin, and trimethoprim-sulfamethoxazole are useful prophylactics. Once travelers have diarrhea, RCTs show that loperamide and zaldaride reduce symptoms and duration; quinolones, ciprofloxacin, norfloxacin, and oral aztreonam reduce abdominal symptoms and time to last liquid stool by several days; azithromycin is effective in treatment of ciprofloxacin-resistant Campylobacter, and trimethoprim-sulfamethoxazole is effective in treating cyclospora. There are no RCTs of preventing accidents and STDs abroad. Health Canada has issued a statement summarizing the risks of acquiring STDs abroad. CONCLUSION: Family physicians can advise their patients on how to reduce risk of travelers' diarrhea and how to treat it themselves on holiday. There is expert advice on how to reduce risk of STDs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".