Adapting The Industrial Stage-Gate <sup>®</sup> Process To Create A Novel Master’S Degree Innovation Curriculum
Bibliographic record
Abstract
This article describes the creation of a novel product-driven master’s degree curriculum in translational medicine based on the industrial Stage-Gate® process. Stage-Gate is an essential tool used by top industrial companies to successfully manage complex development processes for products like medical devices and drugs. Intimate knowledge of this tool is key in the translation of a brilliant concept to a successful product. Currently, Stage-Gate is predominantly taught to high-level executive leadership personnel or in business-related graduate programs. Unfortunately, this “top-down approach” does not leverage the full workforce that is involved in the process. A skilled workforce on all levels, including graduate-level technical experts, is desired by industry to reduce costly ramp-up resources and to boost the attrition rate of successful new products.We adapted the Stage-Gate process to a new and exceptionally visionary master’s degree program in translational medicine. A vertically integrated strategy was utilized to implement Stage-Gate. Industry expert lecturers were assigned to teach Stage-Gate in the context of small and large company environments. The Stage-Gate process itself was integrated into the curriculum schedule to allow continued hands-on practice from a company perspective. Courses were aligned and supplemented to adequately deepen key aspects of the Stage-Gate tool and seamlessly integrate the multidisciplinary curriculum that combines comprehensive core competency in medicine, engineering, and business. Finally, students were required to undergo a formal Stage-Gate review at the completion of each Stage-Gate step. The results illustrate the effectiveness of this adaptation to teach the Stage-Gate tool in a pilot cohort.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".