ReRouting biomedical innovation: observations from a mapping of the alternative research and development (R&D) landscape
Bibliographic record
Abstract
In recent years, the world has witnessed the tragic outcomes of multiple global health crises. From Ebola to high prices to antibiotic resistance, these events highlight the fundamental constraints of the current biomedical research and development (R&D) system in responding to patient needs globally.To mitigate this lack of responsiveness, over 100 self-identified "alternative" R&D initiatives, have emerged in the past 15 years. To begin to make sense of this panoply of initiatives working to overcome the constraints of the current system, UAEM began an extensive, though not comprehensive, mapping of the alternative biomedical R&D landscape. We developed a two phase approach: (1) an investigation, via the RE:Route Mapping, of both existing and proposed initiatives that claim to offer an alternative approach to R&D, and (2) evaluation of those initiatives to determine which are in fact achieving increased access to and innovation in medicines. Through phase 1, the RE:Route Mapping, we examined 81 initiatives that claim to redress the inequity perpetuated by the current system via one of five commonly recognized mechanisms necessary for truly alternative R&D.Preliminary analysis of phase 1 provides the following conclusions: 1. No initiative presents a completely alternative model of biomedical R&D. 2. The majority of initiatives focus on developing incentives for drug discovery. 3. The majority of initiatives focus on rare diseases or diseases of the poor and marginalized. 4. There is an increasing emphasis on the use of push, pull, pool, collaboration and open mechanisms alongside the concept of delinkage in alternative R&D. 5. There is a trend towards public funding and launching of initiatives by the Global South. Given the RE:Route Mapping's inevitable limitations and the assumptions made in its methodology, it is not intended to be the final word on a constantly evolving and complex field; however, its findings are significant. The Mapping's value lies in its timely and unique insight into the importance of ongoing efforts to develop a new global framework for biomedical R&D. As we progress to phase 2, an evaluation tool for initiatives focused on identifying which approaches have truly achieved increased innovation and access for patients, we aim to demonstrate that there are a handful of initiatives which represent some, but not all, of the building blocks for a new approach to R&D.Through this mapping and our forthcoming evaluation, UAEM aims to initiate an evidence-based conversation around a truly alternative biomedical R&D model that serves people rather than profits.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".