Research Priorities for Neglected Infectious Diseases in Latin America and the Caribbean Region
Bibliographic record
Abstract
Global priorities for research in neglected infectious diseases (NIDs) can be assessed in different ways, but it is important to realize that regional priorities may significantly differ one from another. The region of Latin America and the Caribbean (LAC) is - along with Africa and Asia - more affected by NIDs than other regions of the world. Some of the Latin American NIDs are common to other continents, while others are very specific or disproportionately affect the Latin American region [1]-[3] (Table 1). Because of its huge ecological diversity, ongoing environmental changes, and massive migrations, LAC is also a catalyst for the (re-)emergence and spreading of NIDs, both inside and outside the subcontinent. Following a colloquium on NIDs in LAC held in Lima, Peru, between 12 and 14 November 2009, a thematic workshop was organized with the support of the European Commission (EC). It involved 29 scientists (16 from the Americas, two from the Democratic Republic of Congo and India, respectively, and nine from Europe) working on different NIDs and representing several research areas from basic to applied. This report summarizes the consensus comments of the expert group after oral and written consultation. It is envisaged that this document should stimulate a debate within the scientific community and serve as a recommendation for future actions by international or regional funding agencies in the area of NIDs in LAC.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".