Driving Medical Innovation Through Interdisciplinarity: Unique Opportunities and Challenges
Bibliographic record
Abstract
Funding Information: Funding agencies have traditionally rewarded independent scientists proposing research in their field of expertise rather than teams of researchers offering to conduct interdisciplinary projects. Over time, complex problems such as climate change led to increased funding for inter-or multidisciplinary research teams. Some researchers have argued that efforts to make research funding contingent on inclusion of interdisciplinarity leads to inefficiency (7). How successful such interdisciplinary focused funding approaches are remains unclear: the US National Institutes of Health (NIH) reports slightly better outcomes for funding fostering interdisciplinary funded programmes vs. conventional, projects of independent research, whereas the opposite is true for the European Research Council (ERC) (18). Funding for collaborative projects are increasingly available and are internationally well supported. For example, the European Framework Program for Research and Innovation, which includes the “Horizon 2020” (H2020) program, is the world’s largest interdisciplinary funding program (19). In the USA, the National Science Foundation (NSF) (20) and the Clinical and Translational Science Awards (CTSA) Program supports national networks of medical research institutions that collaborate to improve the efficiency of translational research, promoting the integration of underserved populations, and train future translational researchers (21). Funding Information: LW is supported by Arthritis Research UK (21953), Great Ormond Street Children’s Charity and the NIHR Biomedical Research Centres at Great Ormond Street Hospital. ML is supported by New Investigators Awards from the CIHR/KRESCENT and the Canadian Child Health Clinician Scientist Program (CCHCSP). Funding Information: 15. Schunn CD, Crowley K, Okada T. The growth of multidisciplinarity in the cognitive science society. Cogn Sci (1998) 22:107–30. doi: 10.1207/s15516709cog2201_4 16. Rajan DK, Ebner A, Desai SB, Rios JM, Cohn WE. Percutaneous creation of an arteriovenous fistula for hemodialysis access. J Vasc Interv Radiol. (2015) 26:484–90. doi: 10.1016/j.jvir.2014. 12.018 17. Roy ED, Morzillo AT, Seijo F, Reddy SMW, Rhemtulla JM, Milder JC, et al. The elusive pursuit of interdisciplinarity at the human— environment interface. Bioscience (2013) 63:745–53. doi: 10.1093/bioscience/6 3.9.745 18. Ledford H. How to solve the world’s biggest problems. Nature (2015) 525:308– 11. doi: 10.1038/525308a 19. Quests for Interdisciplinarity: A Challenge for the ERA and HORIZON 2020. Publications Office (2015). Available online at: https://www.nap.edu/catalog/ 9942/bridging-disciplines-in-the-brain-behavioral-and-clinical-sciences 20. Introduction to Interdisciplinary Research. NSF - National Science Foundation (2004). Available online at: https://www.nsf.gov/od/oia/ additional_resources/interdisciplinary_research/index.jsp (Accessed July 12, 2018).
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 | 0.085 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.032 | 0.032 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".