Converting inventions into innovations to address cancer grand challenges: The role of scientific and digital search intensity
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
Abstract The present study seeks to shed further light on what favors the conversion of inventions into innovations in for‐profit firms and to advance our understanding of how to tackle cancer grand challenges (CGCs). Specifically, following the literature on knowledge search and recombination, we analyze whether and how cancer‐related inventions developed through an intense adoption of scientific knowledge (scientific search intensity) result in (i) a higher number of approved drugs and (ii) a shorter approval time for new drugs. Notably, while the role of science with regard to technological development has been widely studied, the extent to which science‐based solutions relate to new product introduction, especially in terms of coping with grand challenges such as approved cancer drugs, is less known. Furthermore, considering the digitization of (health) R&D and the role of information and communication technologies (i.e., digital technologies) to address grand challenges, we examine whether and how cancer‐related inventions developed through an intense adoption of digital knowledge (digital search intensity) directly affect the extent and speed of cancer drug approval, as well as whether interaction effects between scientific and digital search intensity exist. We develop hypotheses that we test on a sample of 65,861 cancer‐related patents owned by 139 for‐profit firms, collected from the USPTO Cancer Moonshot Patent Data. These have a priority date between 1990 and 2010, and have led to 1035 approved drugs. Results reveal that scientific search intensity is not associated with the number of different drugs developed from a single cancer‐related invention but is associated with the speed at which the invention leads to a newly approved drug. Digital search intensity appears not to directly affect cancer drug approval, but it lessens the effects of scientific search intensity, thus pointing to a limit of digitization in cancer R&D and innovation processes.
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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 | 0.001 | 0.005 |
| 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