One step forward, one step sideways? Expanding research capacity for neglected diseases
Bibliographic record
Abstract
BACKGROUND: There is general agreement, including from the pharmaceutical industry, that current market based methods of generating research into the development of pharmaceutical products that are relevant for developing countries do not work. This conclusion is relevant not just for the most neglected diseases such as leishmaniasis but even for global diseases such as cancer and cardiovascular disease. DISCUSSION: Stimulating research will mean overcoming barriers such as patent thickets, poor coordination of research activities, exclusive licensing of new technologies by universities and the structural problems that inhibit conducting appropriate clinical trials in developing countries. In addition, it is necessary to ensure that the priorities for research reflect the needs of developing countries and not just donors. This article will explore each of these issues and then look at three emerging approaches to stimulating research -paying for innovation, priority review sales or vouchers and public-private partnerships, - and evaluate their strengths and weaknesses. SUMMARY: All of the stakeholders agree that there is a pressing need for a major expansion in the level of R&D. Whatever that new model turns out to be, it will have to deal with the 5 barriers outlined in this paper. Finally, none of the three proposals considered here for expanding research is free from major limitations.
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 | 0.109 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.019 | 0.047 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.023 | 0.019 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".