Rethinking medical innovation: organizing R&D, responding to crisis, delivering health services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic highlighted the importance of different types of medical innovations. It also increased attention to the policies and practices that drive medical innovation, and which enable rapid development of some essential products while permitting high and rising prices, and persistent unmet needs. Responses to the pandemic thus also triggered a more scientific question: Do we need to rethink more fundamentally how we should understand and investigate medical innovation? This question forms the starting point for the current special issue. This editorial article first reviews three key characteristics of medical innovation: 1) the complex relationship medical innovation has to both demand and need; 2) the critical importance of various forms of scientific knowledge and collaboration; and 3) the centrality of governments and regulations. We next review each individual contribution and highlight how each article touches upon at least two of these key characteristics and prompts reflection on how medical innovation may be rethought. In the final section, the editorial article highlights the unanswered questions that warrant further research from organisation, management, policy, and innovation perspectives. There is still much we need to know about how actors involved in developing and implementing medical innovation can and should respond to crises in general, including what innovation policies and regulations are needed to strengthen innovation capacity, as well as the deep and complex links between medical innovation and the delivery of care.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it