Biotechnology to improve health in developing countries: a review
Bibliographic record
Abstract
The growing health disparities between the developing and the developed world call for urgent action from the scientific community. Science and technology have in the past played a vital role in improving public health. Today, with the tremendous potential of genomics and other advances in the life sciences, the contribution of science to improve public health and reduce global health disparities is more pertinent than ever before. Yet the benefits of modern medicine still have not reached millions of people in developing countries. It is crucial to recognize that science and technology can be used very effectively in partnership with public health practices in developing countries and can enhance their efficacy. The fight to improve global health needs, in addition to effective public health measures, requires rapid and efficient diagnostic tools; new vaccines and drugs, efficient delivery methods and novel approaches to therapeutics; and low-cost restoration of water, soil and other natural resources. In 2002, the University of Toronto published a report on the "Top 10 Biotechnologies for Improving Health in Developing Countries". Here we review these new and emerging biotechnologies and explore how they can be used to support the goals of developing countries in improving health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".