Healthcare's Grand Challenge: Stimulating Basic Science on Diseases that Primarily Afflict the Poor
Bibliographic record
Abstract
Perhaps the most compelling Grand Challenge in healthcare is addressing diseases that primarily afflict the poor. In this paper, we examine the effect of the World Trade Organization's (WTO's) 1994 policy of Trade-Related Intellectual Property Rights (TRIPS), which was justified in part by a claim that patents and other intellectual property protections (IPPs) would improve the availability of drugs for 'neglected diseases' such as malaria and tuberculosis. To date, scholars have found little evidence associating TRIPS with clinical trials, patents, or trade in drugs for neglected diseases. We revisit the original economic logic behind TRIPS and introduce a complementary theory that TRIPS encouraged the time-consuming and complex development of the managerial institutions required for the prerequisite basic science for neglected diseases. We test this logic on a large cross-section of scientific publications. The results indicate an increase in basic science on neglected diseases and in applied science on non-neglected diseases in line with our predictions. Further analysis indicates increases in scientific activity authored in low-income countries on locally relevant neglected diseases. We interpret these results to call for application of theories of management to Grand Challenges, and especially to the evaluation of policies such as TRIPS. Addressing the Grand Challenge of healthcare for the poor depends on interventions that deepen the development of managerial institutions of science.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".