Value of routine dengue diagnosis in endemic countries
Bibliographic record
Abstract
Dengue is one of the most common arthropod-borne viral diseases in humans and it is a leading cause of illness and death in the tropical and subtropical regions of the world. It is thought to account for 400 million cases annually among approximately 3.97 billion people at risk of infection in 128 endemic countries. Despite the global prevalence of the disease, the availability of a vaccine is limited in most countries in the endemic areas. Most endemic countries in South America, South East Asia and Africa serve as attractive touristic sites for people from non-endemic countries who become infected and export the virus to dengue-free regions. Dengue fever typically resembles malaria and in endemic countries most cases of dengue are treated as presumptive malaria. Consequently, routine dengue diagnosis among persons with fever will offer early treatment and reduce the burden of the disease. Also, routine testing among travellers from endemic countries will reduce importation and prevent the geographical expansion of dengue. In this essay, we seek to highlight the usefulness of routine dengue testing in endemic countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".