Preparing your patients to travel abroad safely. Part 3: Reducing the risk of malaria and dengue fever.
Bibliographic record
Abstract
OBJECTIVE: To provide evidence-based recommendations for family physicians advising travelers on how to reduce their risk of malaria and dengue fever. QUALITY OF EVIDENCE: A search of MEDLINE from 1990 to November 1998 found 671 articles; randomized controlled trials and systematic reviews were sought. The Cochrane Collaboration was searched for studies relevant to family physicians; meta-analyses of impregnating bed nets with permethrin were found. Health Canada's evidence-based publications were searched; 10 recommendations based on at least one well-conducted randomized trial were found. MAIN MESSAGE: Good evidence-based advice about the efficacy of mefloquine in chloroquine-resistant areas and for pregnant women and children is available, as is advice on the effectiveness of permethrin-impregnated bed nets. CONCLUSIONS: Family physicians can use evidence-based recommendations to advise their patients on how to prevent malaria. The ways in which patients neglect malaria precautions are well-known. For prevention of both malaria and dengue fever, family physicians should counsel their patients to reduce the risk of being bitten by insects.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".